Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"5392",leadTitle:null,fullTitle:"An Analysis of Contemporary Social Welfare Issues",title:"An Analysis of Contemporary Social Welfare Issues",subtitle:null,reviewType:"peer-reviewed",abstract:"This book offers a sharp critique and a detailed analysis of some pernicious social welfare problems and the wide-ranging causes and consequences of those complex social issues on individuals, families, and communities. Unemployment, health-care disparities, teenage pregnancy, and intimate partner violence constitute the focus of this work. Based on empirical and historical analyses of primary and secondary data, the book provides a conceptual framework that facilitates the reader's understanding of how those social issues are interrelated. Each chapter offers some clear policy recommendations directed to address those social problems. Written by well-published scholars, this work will be of great interest not only to students majoring in the social and political sciences but also to academics and practitioners active in the field of social welfare, social policy, and social work.",isbn:"978-953-51-2719-2",printIsbn:"978-953-51-2718-5",pdfIsbn:"978-953-51-4161-7",doi:"10.5772/62677",price:100,priceEur:109,priceUsd:129,slug:"an-analysis-of-contemporary-social-welfare-issues",numberOfPages:72,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:"4a9795772c4001a5a648421ebf11cee7",bookSignature:"Rosario Laratta",publishedDate:"October 26th 2016",coverURL:"https://cdn.intechopen.com/books/images_new/5392.jpg",numberOfDownloads:14585,numberOfWosCitations:0,numberOfCrossrefCitations:8,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:11,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:19,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 22nd 2016",dateEndSecondStepPublish:"April 12th 2016",dateEndThirdStepPublish:"July 17th 2016",dateEndFourthStepPublish:"October 15th 2016",dateEndFifthStepPublish:"November 14th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,8",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"118227",title:"Dr.",name:"Rosario",middleName:null,surname:"Laratta",slug:"rosario-laratta",fullName:"Rosario Laratta",profilePictureURL:"https://mts.intechopen.com/storage/users/118227/images/2216_n.jpg",biography:'Rosario Laratta is an associate professor of social policy at the School of Governance Studies and the School of Global Governance, Meiji University, Tokyo, & an adjunct faculty at the iCLA (Yamanashi Gakuin University), ICU (International Christian University), Sophia University, Temple University, and recently Toyo University. Before his current appointments, he worked four years for the University of Tokyo. He earned a Postdoctorate in Politics from the University of Tokyo, a PhD and MA in Political Sociology from Warwick University (UK), a MA in Public Policy from Bocconi University, and a BA in Political Science from Calabria University. He is the author of many books such as “Nonprofit Organizations in England and Japan” (2012) and “Empirical Policy Research” (2013), and editor of “Social Welfare” (2012), \\"Social Enterprise\\" (2016), and \\"An Analysis of Contemporary Social Welfare Issues\\" (2016). He has also published over hundred articles, most of which are peer-reviewed papers on leading international journals, such as “Hand in Hand or Under the Thumb?” (Cambridge Journal of Social Policy and Society), “From Welfare State to Welfare Society” (International Journal of Social Welfare), and ““Ethical Climate and Accountability in Nonprofits: a comparative study between Japan and U.K” (Public Management Review). He currently acts as a regular reviewer for fifteen peer-reviewed international journals and as advisory board member for some of those. He is also member of a number of academic associations in Japan and abroad.',institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"3",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1336",title:"Social Services",slug:"social-services"}],chapters:[{id:"52455",title:"Introductory Chapter: An Overview of the Book",doi:"10.5772/65717",slug:"introductory-chapter-an-overview-of-the-book",totalDownloads:1493,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Rosario Laratta",downloadPdfUrl:"/chapter/pdf-download/52455",previewPdfUrl:"/chapter/pdf-preview/52455",authors:[{id:"118227",title:"Dr.",name:"Rosario",surname:"Laratta",slug:"rosario-laratta",fullName:"Rosario Laratta"}],corrections:null},{id:"51989",title:"Policy Discussions on LGBTQ Intimate Partner Violence in North America",doi:"10.5772/64965",slug:"policy-discussions-on-lgbtq-intimate-partner-violence-in-north-america",totalDownloads:1724,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,abstract:"This chapter delves into social policy and welfare regarding intimate partner violence (IPV) across North America, specifically around research, policies, and treatment interventions for the lesbian, gay, bisexual, transgender, and queer (LGBTQ) community. In this chapter, we outline the problem of intimate partner violence, or IPV, in the USA; analyze IPV policies at the state and national levels; and advocate for more specific treatment interventions to address the unique needs of this community.",signatures:"Clare Cannon and Fred Buttell",downloadPdfUrl:"/chapter/pdf-download/51989",previewPdfUrl:"/chapter/pdf-preview/51989",authors:[{id:"187695",title:"Ph.D. Student",name:"Clare",surname:"Cannon",slug:"clare-cannon",fullName:"Clare Cannon"},{id:"187933",title:"Dr.",name:"Fred",surname:"Buttell",slug:"fred-buttell",fullName:"Fred Buttell"}],corrections:null},{id:"52475",title:"Teenage Pregnancies: A Worldwide Social and Medical Problem",doi:"10.5772/65462",slug:"teenage-pregnancies-a-worldwide-social-and-medical-problem",totalDownloads:8293,totalCrossrefCites:6,totalDimensionsCites:8,hasAltmetrics:1,abstract:"Teenage pregnancies and teenage motherhood are a cause for concern worldwide. From a historical point of view, teenage pregnancies are nothing new. For much of human history, it was absolutely common that girls married during their late adolescence and experienced first birth during their second decade of life. This kind of reproductive behavior was socially desired and considered as normal. Nowadays, however, the prevention of teenage pregnancies and teenage motherhood is a priority for public health in nearly all developed and increasingly in developing countries. For a long time, teenage pregnancies were associated with severe medical problems; however, most of data supporting this viewpoint have been collected some decades ago and reflect mainly the situation of per se socially disadvantaged teenage mothers. According to more recent studies, teenage pregnancies are not per se risky ones. A clear risk group are extremely young teenage mothers (younger than 15 years) who are confronted with various medical risks, such as preeclampsia, preterm labor, and small for gestational age newborns but also marked social disadvantage, such as poverty, unemployment, low educational level, and single parenting. In the present study, the prevalence and outcome of teenage pregnancies in Austria are focused on.",signatures:"Sylvia Kirchengast",downloadPdfUrl:"/chapter/pdf-download/52475",previewPdfUrl:"/chapter/pdf-preview/52475",authors:[{id:"188289",title:"Prof.",name:"Sylvia",surname:"Kirchengast",slug:"sylvia-kirchengast",fullName:"Sylvia Kirchengast"}],corrections:null},{id:"52198",title:"Unemployment and Causes of Hospital Admission Considering Different Analytical Approaches",doi:"10.5772/65021",slug:"unemployment-and-causes-of-hospital-admission-considering-different-analytical-approaches",totalDownloads:1477,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The association between unemployment and hospital admission is known, but the causal relationship is still under discussion. The aim of the present analysis is to compare results of a cross-sectional and a cohort approach considering overall hospital admission and hospital admission due to cancer and circulatory disease. Register-based data were analysed for the period of 2006–2009. In the cross-sectional analysis, a multiple logistic regression model was conducted based on the year 2006, and cohort information from the same year onward up to 2009 was available for a Cox regression model. Social welfare compensated unemployment and both types of disease-specific hospital admission were associated to be statistically significant in the cross-sectional analysis. With regard to circulatory disease, the cohort approach suggests that social welfare compensated unemployment might lead to hospital admission due to the disease. Given the significant results in the cross-sectional analysis for hospital admission due to cancer, the unfound cohort effect might indicate a reverse causation suggesting that the disease caused joblessness, and finally social welfare compensated unemployment and not vice versa. Comparing different study designs allows for a better causal interpretation, which should be recommended in future quantitative social welfare analysis.",signatures:"Gabriele Berg-Beckhoff, Gabriel Gulis, Carsten Kronborg Bak and\nPernille Tanggaard Andersen",downloadPdfUrl:"/chapter/pdf-download/52198",previewPdfUrl:"/chapter/pdf-preview/52198",authors:[{id:"188461",title:"Dr.",name:"Gabriele",surname:"Berg-Beckhoff",slug:"gabriele-berg-beckhoff",fullName:"Gabriele Berg-Beckhoff"},{id:"188463",title:"Dr.",name:"Gabriel",surname:"Gulis",slug:"gabriel-gulis",fullName:"Gabriel Gulis"},{id:"188465",title:"Dr.",name:"Carsten",surname:"Kronborg Bak",slug:"carsten-kronborg-bak",fullName:"Carsten Kronborg Bak"},{id:"188466",title:"Dr.",name:"Pernille",surname:"Tangaard Andersen",slug:"pernille-tangaard-andersen",fullName:"Pernille Tangaard Andersen"}],corrections:null},{id:"52228",title:"An Approach to Social Service Systems in Europe: The Spanish Case",doi:"10.5772/65121",slug:"an-approach-to-social-service-systems-in-europe-the-spanish-case",totalDownloads:1598,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter endeavors to develop an attempt at characterizing the social service system in Europe, serving three areas that we understand to be present in different system models but with different logics. The first has to do with the different denominations and ways of defining social services in each country. The second refers to the logic that legitimizes it, referring to its objects and purposes, as well as the type of needs and population groups that are targeted. The third area addresses issues of governance, the way it structures its devices and the relationships it establishes between the different levels of government and the main actors (the third sector, families, and the market). Having established this characterization (following this logic), we arrive at the Spanish case, trying to analyze its current model from legislative transformations that it has developed as well as trends and processes that the system has been generating as a result of the socioeconomic crisis, which have led to the modification of its profiles and demands. Finally, we take a rudimentary approach to the different challenges that we claim the Spanish Public System of Social Services must cope with in the current context.",signatures:"Auxiliadora González Portillo and Germán Jaraíz Arroyo",downloadPdfUrl:"/chapter/pdf-download/52228",previewPdfUrl:"/chapter/pdf-preview/52228",authors:[{id:"187879",title:"Dr.",name:"Auxiliadora",surname:"González Portillo",slug:"auxiliadora-gonzalez-portillo",fullName:"Auxiliadora González Portillo"},{id:"189164",title:"Dr.",name:"Germán",surname:"Jaraíz Arroyo",slug:"german-jaraiz-arroyo",fullName:"Germán Jaraíz Arroyo"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5145",title:"Social Enterprise",subtitle:"Context-Dependent Dynamics In A Global Perspective",isOpenForSubmission:!1,hash:"ca8890940e640527f9fce7ed5e6a51b1",slug:"social-enterprise-context-dependent-dynamics-in-a-global-perspective",bookSignature:"Rosario Laratta",coverURL:"https://cdn.intechopen.com/books/images_new/5145.jpg",editedByType:"Edited by",editors:[{id:"118227",title:"Dr.",name:"Rosario",surname:"Laratta",slug:"rosario-laratta",fullName:"Rosario Laratta"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1848",title:"Social Welfare",subtitle:null,isOpenForSubmission:!1,hash:"67f5dd4197c0618919f6150b7099846b",slug:"social-welfare",bookSignature:"Rosario Laratta",coverURL:"https://cdn.intechopen.com/books/images_new/1848.jpg",editedByType:"Edited by",editors:[{id:"118227",title:"Dr.",name:"Rosario",surname:"Laratta",slug:"rosario-laratta",fullName:"Rosario Laratta"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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\r\n\tAll the industrial processes (manufacturing, mechanization, handling…) are changing rapidly as the new technologies evolve. The introduction of Industry 4.0 concepts is changing the classical concept of industry to a new one, more connected, more concentrated in the collaborative work between humans and robots and focused to adopt robotic technologies in the SME.
\r\n
\r\n\tThis book is intended to cover those previous aspects and others related to the new challenges that appear in robotizing the present industry at all levels. Contributions are welcome regarding new robotic morphologies and mechanics to be adopted to new tasks that the collaboration between humans and robots is introducing. This collaboration needs also a lot research that editors expect to collect in the book as well. Control and programming robots to be more efficient and accurate in the tasks also need new research that is expected to be covered in the book. New applications and new robots used in other fields can be now applied into industrial domain. Regarding this point, unmanned aerial robots are deployed more usually for inspections, maintenance, control, surveillance tasks in the industry world. Navigation, map generation and collaboration are also new fields that need research and some papers are expected to be published on these topics in the book. The new applications and the use of new robot structures lead to a review and assessment in terms of ethics and values. Education is still a pending issue that need to be covered at a undergraduated level also, and new educational experiences in this direction are expected as well.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"4a56dacb7c0504f0601f63000d64c1bc",bookSignature:"Prof. Antoni Grau and Dr. Rodrigo Munguia",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10630.jpg",keywords:"New Morphologies, Mechanical Structures, New Control Strategies, Modeling and Identification, Cooperative-Collaborative-Cobots, Human-Robot Interaction, Slam, Robot Communications, Autonomous Cars, UAV and USV, Ethics, Education and Training",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 27th 2020",dateEndSecondStepPublish:"November 24th 2020",dateEndThirdStepPublish:"January 23rd 2021",dateEndFourthStepPublish:"April 13th 2021",dateEndFifthStepPublish:"June 12th 2021",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:1,editedByType:null,kuFlag:!1,biosketch:"Dr. Grau is a robotics academic and researcher with many journal publications, he also edited several books and is a senior IEEE member serving as an associate editor for the IEEE Transactions on Industrial Informatics.",coeditorOneBiosketch:"Dr. Munguia is a researcher in mobile robotics, former head of the Control's Systems and Artificial Intelligence Center of the University of Guadalajara, and author of more than fifty scientific and technical papers among international journals, chapter books, and conference proceedings.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"13038",title:"Prof.",name:"Antoni",middleName:null,surname:"Grau",slug:"antoni-grau",fullName:"Antoni Grau",profilePictureURL:"https://mts.intechopen.com/storage/users/13038/images/system/13038.jfif",biography:"Antoni Grau received his M.S. and Ph.D. degrees in computer science from the Technical University of Catalonia (UPC), Barcelona, in 1990 and 1997, respectively. He is currently a Professor with the Department of Automatic Control, UPC, giving lectures on computer vision, digital signal processing, and robotics at the School of Informatics of Barcelona. His research interests include computer vision, pattern recognition, autonomous mobile robots, factory automation, and education on sustainable development. He has chaired several international conferences. 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1. Introduction
The development of convenient methodologies for simultaneous detection of specific multi-DNAs and antigens in biological and environmental samples has received broad attention because their detection, identification and quantification are very complex, expensive and time consuming (Abu-Salah et al., 2010; Strehlitz et al., 2008; Zhang et al., 2009). Over the past 30 years, biosensors, namely the devices incorporating biological sensing elements either intimately connected to or integrated within transducers, have been designed and fabricated to find effective solutions to these problems, which offer certain operational advantages over other traditional methods, notably with respect to rapidity, ease-of-use, low cost, simplicity, portability, and ease of mass manufacture (Cooper, 2002; Turner, 2000; Turner et al., 1987). The fundamental prerequisite of biosensors depends on specific molecular recognition based on affinity between complementary structures such as enzyme-substrate, antibody-antigen, receptor-hormone, and so forth (Zhang et al., 2009). This specific recognition gives rise to the production of concentration–proportional signals. Up to date, some biosensors have been commercialized for some special applications like blood glucose and lactate measurement or bioprocess control, amongst others. However, they have not still entered the market as much as expected, which is caused by the following reasons: (1) the selectivity and specificity of biosensor highly depends on biological recognition systems (Spichiger-Keller, 1998); (2) the sensitivity detection limit is difficult to achieve trace levels, even the single molecular detection (Sheehan and Whitman, 2005); (3) High throughput assay is desired to simultaneously process multiple samples (Sittampalam et al., 1997); (4) the biological recognition elements of the biosensor (e.g. enzymes, antibodies or cells) are usually instable (So et al., 2005).
With the development of nanoscience and nanotechnology, a series of novel nanomaterials with controlled size and morphologies are being fabricated, their novel properties are being gradually discovered with difference from their corresponding bulk materials, and the applications of nanomaterials in biosensors have also made great advances (Jianrong et al., 2004; Kumar, 2007; Pandey et al., 2008). Nanomaterials can be made from both inorganic and organic materials and are less than 100 nm in length along at least one dimension (Asefa et al., 2009; Zhong, 2009). This small size scale leads to large surface areas and unique size-related optical properties. For example, the quantum confinement effects that occur in nanometer-sized semiconductors widen their band gap and generate well-defined energy levels at the band edges, causing a blue-shift in the threshold absorption wavelength with decreasing particle size and inducing luminescence that is strictly correlated to particle size (Krishna and Friesner, 1991; Peng et al., 2000). Therefore, the position of the absorption as well as the luminescence peaks can be fine-tuned by controlling the particle size and the size distribution during synthesis, generating a large group of “fluorophores” with diverse optical properties (Nirmal and Brus, 1999; Pradhan et al., 2005). The size- or shape-controllable optical characteristics of nanomaterials facilitate the selection of diverse probes for higher throughput assay (Zhong, 2009). Furthermore, the nanostructure can provide a substrate support for sensing assays with multiple probe molecules attached to each nanostructure, simplifying assay design and increasing the labeling ratio for higher sensitivity (Kumar, 2007). Therefore, nanomaterials have opened up new horizons for biosensors.
Biosensors based on nanomaterials, which represent the integration of material science, molecular engineering, chemistry and biotechnology, can markedly improve the sensitivity, selectivity, specificity and rapidity of bio-molecular detection, offer the promising capability of detecting or manipulating atoms and molecules, and have great potential in the development of the miniaturizebility or portability of analytical system (Zhang et al., 2009). Previous studies have shown that combining the specific molecular recognition ability of biomolecules with the unique structural and photophysical characteristics of inorganic or organic nanomaterials, such as nanocrystals, nanotubes, nanowires, nanomicelles, and nanovesicles, can create new types of analytical tools (Niemeyer, 2001). So far, the nanomaterials are widely used in biosensors mainly including (1) carbon nanomaterials (e.g., fullerenes, carbon nanotubes, carbon nanohorns, graphene, etc.) (Chen et al., 2003; Vamvakaki and Chaniotakis, 2007; Yang et al., 2010b), (2) metallic nanomaterials (e.g., quantum dots, gold nanoparticles, gold nanorods, europium nanoparticles, etc.) (Ao et al., 2006; Liu, 2009; Pan et al., 2005), (3) silica nanomaterials (Slowing et al., 2007), (4) organic polymer nanomaterials (e.g., molecular imprinted polymers) (Hatchett and Josowicz, 2008), or (5) supramolecular aggregates (nanomicelles, nanovesicles) (Kuhn, 1994). Fig. 1 illustrates the correlation between these typical nanomaterials and the major properties exploited for analytical purposes (Valcarcel et al., 2008).
2. Carbon nanotubes as analytical tools
Since their discovery by Iijima in 1991, Carbon nanotubes (CNTs), due to the remarkable structure-dependent electronic, mechanical, optical, and magnetic properties, have triggered intensive studies directed towards numerous applications including nanoelectronics, biomedical engineering, biosensing, and bioanalysis (Dai, 2002; Iijima, 1991). CNTs are rolled up seamless cylinders of graphene sheets. According to the number of graphene layers, CNTs are classified into single-walled carbon nanotubes (SWNTs) and multi-walled carbon nanotubes (MWNTs) (Liu et al., 2009). Their lengths can range from several hundred nanometers to several micrometers, and the diameters from 0.2 to 2 nm for SWNTs and from 2 to 100 nm for coaxial MWNTs (Valcarcel et al., 2005). So far, CNTs has been used as an analytical tool to improving the analytical process (Valcarcel et al., 2007). CNTs-based biosensors have been developed to detect biological species including proteins and DNA. Fig. 2 illustrates their potential roles in the development of new tools for analytical science, arranged in terms of complexity of design and integration (Valcarcel et al., 2007).
Figure 1.
Correlation between typical nanomaterials and the major property exploited for analytical purposes (Valcarcel et al., 2008). (With permission from Springer)
Figure 2.
Classification of analytical tools based on the use of carbon nanotubes according to the analytical complexity and the increasing level of system design and integration (Valcarcel et al., 2007). (With permission from American Chemical Society)
Generally, CNTs are expected to be controllably assembled into designed architectures as integral components of composites or supramolecular structures (Grzelczak et al., 2006). The integration of biomaterials (e.g., proteins/enzymes, antigens/antibodies, or DNA) with CNTs provides new hybrid systems that combine the conductive or semiconductive properties of CNTs with the recognition or catalytic properties of the biomaterials in Fig. 3 (Katz and Willner, 2004). CNTs can provide scaffolds for biomolecules immobilization, allowing subsequent applications in biosensors, utilizing the intrinsic electronic or optical properties of CNTs for signal transduction (Cataldo, 2008). Due to CNTs’ the high surface area, semiconducting behavior, band gap fluorescence, and strong Raman scattering spectra, the proximal or adsorbed biomolecules can be measured or detected easily, when their interactions along the CNTs sidewall, at functionalized cap regions (Cheung et al., 1998), and even within the nanotube shell (Lin et al., 2004; Liu et al., 2009). Proximity of reasonably charged or polarized biomolecules yields gating effects on isolated semiconducting CNTs, or net semiconducting networks of CNTs, thus yielding field-effect transistors (FETs) capable of quantifying the degree of specific or non-specific binding of biomolecules (Chen et al., 2003; Liu et al., 2009). Additionally, CNTs were also used as analytical targets. After specific conjugation of targeting ligands to SWNT tags or coupled with sufficient sidewall passivation in order to prevent non-specific binding, the photoluminescent (FL) and Raman scattering properties of SWNTs can be detected as target signals in biosensors (Liu et al., 2009; Valcarcel et al., 2007). Therefore, CNTs are of great interest for the development of highly sensitive and multiplexed biosensors for applications in analytical chemistry.
Figure 3.
The conceptual generation of biomolecules-carbon nanotubes conjugates, and their assembly to yield functional devices (Katz and Willner, 2004). (With permission from Wiley-VCH)
3. Quantum dots as analytical tools
Since their discovery in the early 1980s, semiconductor nanomaterials or quantum dots (QDs), have been extensively used as potential luminescence probes due to their high resistance to photobleaching, narrow emission spectra, broad excitation spectra, and longer fluorescence lifetime in the field of biosensing and imaging (Alivisatos, 1996). QDs are usually composed of atoms of elements from groups II to VI (e.g., Cd, Zn, Se, Te) or III–V (e.g., In, P, As) in the periodic table (Chan et al., 2002). As a result of their very small (< 10 nm) dimensions, QDs exhibit quantum confinement effects that are responsible for their wide UV-visible absorption spectra, narrow emission bands, and optical properties, which can be tuned by size, composition, and shape (Alivisatos, 2003; Chan and Nie, 1998). These features come with high flexibility in the selection of excitation wavelength as well as minimal overlap in the emission spectra from multiple QDs, making them excellent labels for high throughput screening (Chan et al., 2002; Han et al., 2001). Additionally, choosing excitation wavelengths far from the emission wavelengths can eliminate background scattering.
Compared with organic fluorophores, QDs have similar quantum yields but extinction coefficients that are 10∼50 times larger, and much-reduced photobleaching rates. The overall effect is that QDs have 10∼20 times brighter fluorescence and about 100∼200 times better photostability (Gao et al., 2005; Zhong, 2009). For applications in biomedical studies, QDs should be water soluble, which can be achieved in two ways: the first is to directly synthesize QDs in aqueous solution; the other is to synthesize QDs in organic solvents and then transfer the hydrophobic QDs into aqueous solution, for example, by ligand exchange or polymer coating (Zhang et al., 2010). So far, several methods have been developed to synthesize water-soluble quantum dots for use in cellular imaging, immunoassays, DNA hybridization and optical bar-coding (Drbohlavova et al., 2009; Yang et al., 2009). Moreover, QDs also have been used to study the interaction between protein molecules or detect the dynamic course of signal transduction in live cells by fluorescence resonance energy transfer (FRET) (Chan and Nie, 1998).
Because QDs are intrinsically fluorescent, they can be widely employed as the reporter molecules for biomolecules detection (Chan and Nie, 1998). For example, QDs-based western blot detection kits can improve the sensitivity detection limit as low as 20 pg protein per lane (Ornberg et al., 2005). The detection limits is around hundreds of picograms of protein per lane in the colorimetric or chemiluminescent detection. So the QDs-based protocol is more sensitive with better image quality in the same measuring time. The test samples can be stored for longer time after staining with minimal loss of signal (Edgar et al., 2006). According to the high resistance of QDs to photobleaching, Genin et al. have reported that the organic dye CrAsH conjugated QDs nanohybrids serve as a probe to bind efficiently and selectively to Cys-tagged proteins and subsequently trace them for more than 150 s, where the fluorescence emission of CrAsH displayed a significant increase after the interaction between CrAsH and cysteine (Genin et al., 2008). While the latter faded rapidly under continuous excitation, emission of the QDs remained unaffected. The persistent fluorescence of the QDs should thus allow extended monitoring of the target protein. In particular, the use of multicolor QDs probes in immunohistochemistry (IHC) is considered one of the most important and clinically relevant applications. Nie et al. have developed antibody-conjugated QDs for multiplexed and quantitative (or semi-quantitative) IHC, and have achieved five-color molecular profiling on formalin-fixed and paraffin-embedded (FFPE) clinical tissue specimens (Xing et al., 2007). They have also optimized the experimental procedures for QDs bioconjugation (Fig. 4), tissue specimen preparation, multi-color staining, image processing and analysis, and biomarker quantification.
Figure 4.
Schematic diagrams showing various methods for QDs-antibody (QD-Ab) bioconjugation. (a)QDs conjugation to antibody fragments via disulphide reduction and sulfhydryl-amine coupling; (b) covalent coupling between carboxylic acid (-COOH) coated QDs and primary amines (-NH2) on intact antibodies using EDAC as a catalyst; (c) site-directed conjugation via oxidized carbohydrate groups on the antibody Fc portion and covalent reactions with hydrazide-modified QDs; (d) conjugation of histidine-tagged peptides or antibodies to Ni-NTA modified QDs; and (e) noncovalent conjugation of streptavidin-coated QDs to biotinylated antibodies (Xing et al., 2007). (With permission from Nature)
Recently, our group have reported a quick and parallel analytical method based on QDs for ToRCH-related antibodies including Toxoplasma gondii, Rubella virus, Cytomegalovirus and Herpes simplex virus type 1 (HSV1) and 2 (HSV2) (Yang et al., 2009). We fabricated the microarrays with the five kinds of ToRCH-related antigens and used CdTe QDs to label secondary antibody and then analyzed 100 specimens of randomly selected clinical sera from obstetric outpatients (Fig. 5). In comparison with enzyme-linked immunosorbent assay (ELISA) kits, the QDs labeling-based ToRCH microarrays display comparable sensitivity and specificity with ELISA. Besides, the microarrays hold distinct advantages over ELISA test format in detection time, cost, operation and signal stability.
Figure 5.
The microarray results with corresponding control sera (a negative serum; b positive control serum of Toxoplasmosis; c positive control serum of Cytomegalovirus; d positive control serum of Rubella virus; e positive control serum of Herpes simplex virus type 1; f positive control serum of Herpes simplex virus type 2) (Yang et al., 2009). (With permission from Springer)
Additionally, we developed a novel fluorescent POC (Point Of Care) test method to be used for screening for syphilis, combining the rapidness of lateral flow test and sensitiveness of fluorescent method (Yang et al., 2010a). 50 syphilis-positive specimens and 50 healthy specimens conformed by Trep-onema pallidum particle agglutination (TPPA) were tested with QDs-labeled and colloidal gold-labeled lateral flow test strips, respectively. Our results showed that both sensitivity and specificity of the QDs–based method reached up to 100% (95% confidence interval [CI], 91~100%), while those of the colloidal gold-based method were 82% (95% CI, 68–91%) and 100% (95% CI, 91–100%), respectively. We found that the naked-eye detection limit of QDs–based method could achieve 2 ng/mL of anti-TP47 polyclonal antibodies purified by affinity chromatography with TP47 antigen, which was ten-fold higher than that of colloidal gold–based method (Fig. 6).
Figure 6.
Result of detection of clinical specimens by colloidal gold and QDs (Yang et al., 2010a). result of detection by colloidal gold: 1 positive; 2 negative; 3 weak positive; result of detection by QDs: 4 positive; 5 negative; 6 weak positive. (With permission from Springer)
Due to their bright intensity and high photostability, QDs also have a wide range of applications in bioimaging (Cui et al., 2009). Our group has successfully prepared the dendrimer-modified QDs with water-soluble, high quantum yield, and good biocompatibility. Our results indicated that arginine-glycine-aspartic acid (RGD) conjugated QDs can specifically target human umbilical vein endothelial cells (HUVEC) and A375 melanoma cells, as well as nude mice loaded with A735 melanoma cells (Li et al., 2010b). Meanwhile, we have successfully synthesized dendrimer-modified QDs nanocomposites, and developed a class of aptamers-conjugated nanoprobes, which could specifically bind with U251 glioblastoma cells and exhibit in vitro molecular imaging (Li et al., 2010a).
4. Carbon nanotubes and quantum dots nanohybrids as analytical tools
Sensitive detection of target analytes present at trace levels in biological samples often requires the labeling of reporter molecules with fluorescent dyes, because fluorescence detection is by far the dominant detection method in the field of sensing technology, due to its simplicity, the convenience of transducing the optical signal, the availability of organic dyes with diverse spectral properties, and the rapid advances made in optical imaging (Zhong, 2009). However, it can be difficult to obtain a low detection limit in fluorescence detection due to the limited extinction coefficients or quantum yields of organic dyes and the low dye-toreporter molecule labeling ratio.
Currently, fluorescence (Förster) resonance energy transfer (FRET) and photoinduced electron transfer (PET) have been widely studied as novel fluorescence detection techniques in biosensors (Zhang et al., 2010). In FRET sensing systems, QDs normally act as donor and transfer excitation energy to a vicinal fluorophore acceptor, leading to a reduced donor PL and a concomitant increased acceptor PL (Fig. 7). In PET sensing systems, the excited QDs act as the electron donor and transfer the excited electrons to acceptor molecules. This in turn results in a quenched QD PL and an increased acceptor PL (if possible). In both, FRET and PET sensing systems, the most significant advantage is that the transfer efficiencies can be used as a ratiometric readout without demanding an extra reference. The second advantage is that such sensing systems are more flexible and enable more complicated designs, resulting from the multiple components involved, e.g., donor, acceptor, and spacers.
Figure 7.
FRET-based sensing (Zhang et al., 2010). a) CdSe QDs and CNTs are conjugated with two oligonucleotides. The two oligonucleotides can be hybridized upon binding a target DNA fragment. In the absence of target DNA, the QDs have its original PL. In the presence of the target DNA fragment, hybridization brings QDs and CNTs close enough so that the QDs PL is quenched by the CNTs. b) Streptavidin modified commercial QDs ‘decorated’ with biotinylated DNA fragments. An ATP aptamer binds with one side to the DNA attached to the QDs and with one side to Cy5-labeled DNA. Upon excitation of the QDs donor, FRET reduces the QDs PL and increases the PL of the Cy5 acceptor. In the presence of ATP molecules, the aptamer dehybridizes and complexes the ATP. As soon as the Cy5-labeled DNA becomes detached from the ATP aptamer, the distance to the QDs is increased and there is no longer FRET. (With permission from Springer)
Based on FRET, Wang et al. combined positively charged CdTe QDs capped with cyst-amine with negatively charged gold nanoparticles (GNPs) capped with 11-mercaptoundecanoic acid (MUA) in a FRET system, the PL of donor QDs can be quenched by the close acceptor GNPs, which is due to the high extinction coefficient and broad absorption of GNPs (Wang and Guo, 2009). Firstly, QDs and GNPs assemble into aggregates due to their mutual electrostatic attraction. Then the Pb2+ was added, MUA-modified GNPs chelated with it into aggregates, the QDs were released and the restored PL was read out. Finally, the PL changes of the QDs were instead of detecting changes. QD-based FRET sensors have been investigated by Mattoussi (Medintz et al., 2003) and Willner (Patolsky et al., 2003) groups. Our group also designed a unique, sensitive, and highly specific fluoroimmunoassay system for antigen detection using GNPs and QDs nanoparticles (Huang et al., 2010). To demonstrate its analytical capabilities, the CdTe QDs were coated with anti-HBsAg monoclonal antibodies (QDs-MAb1) and GNPs coated with another anti-HBsAg monoclonal antibodies (GNPs-MAb2) which specifically bound with HBsAg could sandwich the HBsAg captured by the immunoreactions. The sandwich-type immunocomplex was formed and the fluorescence intensity of QDs was measured. The results showed that the fluorescence intensity of QDs at 570 nm was negative linear proportional to the HBsAg concentration logarithm, and the limit of detection of the HBsAg was 0.928 ng/mL. This new system can be extended to detect target molecules with matched antibodies and has broad potential applications in immunoassay and disease diagnosis.
CNTs represent one type of unique nanomaterials used in fluorescence-based bioassays. The sensing utilizes the ability of CNTs to quench organic dyes or QDs (Pan et al., 2006b; Pan et al., 2008). Tang group reported that organic dyes could be quenched by CNTs through an energy transfer mechanism (Yang et al., 2008). This feature was employed to develop a non-covalent assembly between CNT and ssDNA for effective sensing of biomolecule interactions (Fig. 8). The strong interaction between CNT and ssDNA quenched the fluorophore conjugated on ssDNA. Hybridization of a complimentary DNA strand or binding of an interactive protein caused ssDNA to be released from the CNT, leading to the restoration of fluorescence signal in increments relative to the fluorescence without a target. The signaling mechanism makes it possible to detect the target by fluorescence spectroscopy.
Figure 8.
A Scheme for signaling biomolecular interactions using an assembly between SWNTs and dye-labeled ssDNA. P1 and P2, the FAM-labeled oligonucleotides; P2, the thrombin-binding aptamer; T1 and T2, the perfect cDNA (T1) and one mismatched DNA (T2) of P1. B Fluorescence emission spectra (λex=480 nm) of P1 (50 nM) under different conditions: (a) P1 in PBS; (b) P1 + 300 nM T1; (c) P1 + SWNTs; and (d) P1 + SWNTs + 300 nM T1. Inset: fluorescence intensity ratio of P1 (b) and P1-SWNTs with F/F0−1 plotted against the logarithm of the concentration of T1 (Yang et al., 2008; Zhong, 2009). (With permission from American Chemical Society and Springer)
Recently, nanohybrids containing both semiconductor QDs and CNTs have been the subject of great interest as a consequence of the development of methods for the chemical modification of CNTs and the seeking for novel functional materials in biosensors (Cui, 2007; Pan et al., 2009). When QDs were binding to CNTs, CNTs could promote direct charge transport and efficient charge transfer from the QDs. This system has the potential to significantly increase the efficiency of photovoltaic devices (Kamat, 2007). Besides, CNTs binding with QDs together can provide one kind of novel nanomaterials–luminescent CNTs can afford fluorescent labels and be utilized for real-time detection, molecular imaging and cell sorting in biological applications (Guo et al., 2008; Shi et al., 2006). Our group has synthesized luminescent CNTs as a new functional platform for bioanalytical sciences and biomedical engineering (Cui et al., 2010).
To further understand that how the nanohybrid structure affects the charge transfer and energy transfer behaviors between the QDs and CNTs, the interactions between QDs (such as CdS, CdSe and CdTe) and CNTs have been carefully investigated by several groups (Guldi et al., 2006; Li et al., 2006b; Robel et al., 2005; Sheeney-Haj-Ichia et al., 2005; Si et al., 2009). The charge-transfer efficiencies were evaluated by studying the changes in the photoluminescence (Li et al., 2006b) or photo-electrochemical properties of hybrid materials (Pan et al., 2008). Studies indicated that strong PL quenching by charge-transfer mechanism were found in the CdS/TOAB/CNT (Guldi et al., 2005), CdSe/pyridine/CNT (Li et al., 2006a) and CdSe/pyrene/CNT (Hu et al., 2008) system. Partial emission quenching was observed on nanohybrids consisting of dendron-modified CdS QDs on CNTs (Hwang et al., 2006). In contrast, Marek Grzelczak et al. reported a reproducible procedure based on the combination of both polymer wrapping and LbL self-assembly techniques for the deposition of CdTe nanocrystals onto CNTs, yielding linear colloidal CdTe–CNT composites with a high degree of coverage (Grzelczak et al., 2006). Although quenching of PL from CdTe occurs when the nanocrystals are directly assembled on the CNTs, such quenching can be controlled through the growth of a silica-shell spacer between the CNT surface and the deposited QDs. The main general steps of this method for the deposition of CdTe QDs onto CNTs and onto silica-coated CNTs (CNTs@SiO2) are summarized in Fig. 9.
Figure 9.
Various possible routes for the preparation of CdTe-CNT and CdTe–CNT@SiO2 nanocomposites. PSS: poly (sodium 4-styrene sulfonate); PDDA: poly (diallyldimethylammoniumchloride) (Grzelczak et al., 2006). (With permission from Wiley-VCH)
The previous works have shown that the PL properties of the QD/CNT nanohybrids are strongly dependent on QD-CNT separation, but precise control to the distance between QDs and CNT was difficult to achieve in the available systems. Recently, Hao-Li Zhang et al. reported a facile strategy for attaching CdSe QDs onto CNT surface by electrostatic self-assembly (Fig. 10) (Si et al., 2009). By using different mercaptocarboxylic ligands, the shell thicknesses of the CdSe QDs are well controlled within angstrom-level precision. The efficiency of the PL quenching decreases upon increasing the shell thickness due to the distance-dependent electron transfer efficiency. This work demonstrates that the shell thickness control to the QDs opens up a straightforward methodology for investigating the interaction between fluorescent nanomaterials coupled with CNTs.
Figure 10.
Schematic illustration to the structures of the CdSe/CNT nanohybrids prepared by electrostatic assembly (left). The photo-induced charge-transfer efficiency within the nanohybrids is controlled by the shell thickness. Energy-level diagram and possible charge-transfer process for the conjugate complex between CdSe QDs and semiconducting CNTs (s-CNT) or metallic CNTs (m-CNT) are illustrated in the right (Si et al., 2009). (With permission from Springer)
An investigation of the effects of MWNTs on the PL properties of CdSe QDs showed that CNTs could suppress the PL of QDs through both dynamic and energy transfer quenching mechanisms (Cui et al., 2008; Pan et al., 2006b; Pan et al., 2008). In order to potentially exploit this feature in bioassays, we reported a novel ultrasensitive DNA or antigen detection strategy based on the CNT-QD assembly (Cui et al., 2008). MWNTs and QDs, their surfaces functionalized with oligonucleotide DNA or antibody (Ab), can be assembled into nanohybrid structure upon the addition of a target complementary oligonucleotide or antigen (Ag). Nanomaterials building blocks that vary in chemical composition, size, or shape are arranged in space on the basis of their interactions with complementary linking oligonucleotides for potential application in biosensors. We show how this oligonucleotide-directed assembly strategy could be used to prepare binary (two-component) assembly materials comprising two differently shaped oligonucleotide-functionalized nanomaterials. Importantly, the proof-of-concept demonstrations reported herein suggest that this strategy could be extended easily to a wide variety of multicomponent systems.
5. Experimental section
5.1. Preparation of water-soluble CdSe QDs
The colloidal CdSe QDs were dissolved in chloroform and reacted with glacial mercaptoacetic acid (1.0 M) for 2 h. Water was added to this reaction mixture at a 1:1 volume ratio. After vigorous shaking and mixing, the chloroform and water layers separated spontaneously. The aqueous layer, which contained mercaptoacetic-coated QDs, was extracted (figure 11). Excess mercaptoacetic acid was removed by four or more rounds of centrifugation.
Figure 11.
Water-Soluble CdSe QDs, Preparation of CNT-COOH, CNT-OH, and CNT-NH2, and Electrostatic Interaction between CNT-NH2 and QDs. (With permission from American Chemical Society)
5.2. Preparation of CNT-COOH, CNT-OH, and CNT-NH2
A 100 mg amount of pristine CNTs was added to aqueous HNO3 (10.0 mL, 60%). The mixture was placed in an ultrasonic bath for 30 min and then stirred for 24 h while being boiled under reflux. The mixture was then in vacuum filtered through a 0.22 μm Millipore polycarbonate membrane and subsequently washed with distilled water until the pH of the filtrate was ca. 7. The filtered solid was dried under vacuum for 12 h at 60 °C. Dried CNT-COOH was suspended in SOCl2 (20 mL) and stirred for 24 h at 65 °C. The solution was filtered, washed with anhydrous THF, and dried under vacuum at room temperature for 24 h, generating CNT-COCl. Dried CNT-COCl was mixed with excess ethylene glycol and stirred for 48 h at 120 °C. The resulting solid was separated by vacuum filtration using a 0.22 μm Millipore polycarbonate membrane filter and subsequently washed with anhydrous THF. After repeated washing and filtration, the resulting solid was dried overnight in a vacuum, generating CNT-OH. On the other hand, CNT-COCl was reacted with ethylenediamine to obtain CNT-NH2 (figure 11).
5.3. Preparation of CNT-DNA probe
Binding of the aminoalkyloligonucleotide, in PBS buffer (0.1 M NaCl, 10 mM phosphate buffer, pH 7.0), to the CNT was accomplished by adding 50 μL of 1 μM aminoalkyloligonucleotide to 1.0 mL of 1 mg/L CNT-COCl in phosphate buffer solution (PBS), incubated for 40 h at 25 °C, collected by centrifugation at 4000 rpm for 10 min, and resuspended in 1 mL PBS to form CNT-oligonucleotide composite (probe 1).
Figure 12.
Surface functionalization with oligonucleotide, and subsequent addition of target oligonucleotide to form CNT-QD assembly. Target 1, complementary DNA; target 2, non-complementary DNA. (With permission from American Chemical Society)
5.4. Preparation of QD-DNA probe
The colloidal CdSe QDs were dissolved in chloroform and were reacted with glacial mercaptoalkyloligonucleotide (1.0 μM) for 2 h. PBS buffer was added to this reaction mixture at a 1:1 volume ratio. After vigorous shaking and mixing, the chloroform and PBS buffer layers separated spontaneously. The PBS buffer layer, containing mercaptoalkyloligonucleotide-coated QDs (probe 2), was extracted from chloroform liquid.
5.5. Preparation of CNT-Ab probe
For the covalent immobilization of antibody on the CNT surface, CNT-NH2 was exposed to 20 nM anti-BRCAA1 IgG in PBS buffer (pH 7.4) overnight at room temperature, rinsed thoroughly in deionized water for 6 h, and then dried with nitrogen gas.
5.6. Preparation of QD-Ab probe
The QDs-pAb probes were prepared by adding BRCAA1 pAb (40 μg) to an aqueous solution of QDs (5 mL, 2.33 nM) at pH 9.0 for 30 min. Then, the solution was treated with 0.5 mL 10% BSA solution for a night to passivate and stabilize the QDs. The final QDs-pAb probes were re-dispersed in 0.01 M PBS at pH 7.4. CNTs-pAb probes also were prepared by above-mentioned method, and the pAb concentration was 1.5 mg/mL.
5.7. Photoluminescence (PL) measurement of CdSe QDs binding to CNTs
CNTs with concentrations from 0 to 100 mg/L were added to the CdSe aqueous solution. QD-CNT solutions were incubated at room temperature in the dark for 3 h. PL spectra were taken with a Perkin-Elmer LS-55 spectrofluorometer. Samples were thermostated at 25 °C. An excitation wavelength of 460 nm was used. The emission spectra were recorded from 500 to 650 nm. The excitation and emission slit widths were set to 5 and 5 nm, respectively. Samples were contained in 1 cm path length quartz cuvettes and continuously stirred. For control experiments, CNTs were removed by centrifugation, the same buffer solution without nanotubes was added to the CdSe solution, and then the changes of PL were recorded.
5.8. Assembly and characterization of CNTs and QDs through DNA hybridization (System 1)
The methodology of DNA detection by fluorescence measurement is shown in System 1. Two microliters of target oligonucleotide with different concentrations were added into the mixture of CNT-DNA (50 μL, [CNT] ) 1 mg/mL, [DNA] ) 0.1 nM) and QD-DNA probes (50 μL, [DNA] ) 0.1 nM). This was mixed and incubated at 75 °C for 5 min and then at 25 °C for 20 min. After hybridization with target DNA, CNT-QD assemblies were formed and then removed by centrifugation at 2000 rpm for 5 min. The unbound QD-DNA probes in supernatant PBS buffer were immediately detected by spectrofluorometer. The wavelength λ = 480 nm of the laser source was used for the excitation of the CNT-QD detection system. The fluorescence signal was recorded over a range from λ = 450 nm to λ = 700 nm. NoncDNA target (CGC GAT CTC AGG TCG ATA GG) was used as the control experiment.
5.9. Three-component CNT-QD system with the purpose of detecting three different DNA targets simultaneously (System 2)
Three QDs with different emission wavelengths at 510, 555, and 600 nm were used to simultaneously detect three target DNA molecules, as shown in System 2, called QD510, QD555, and QD600 probes, respectively. There are six probes in this system: three CNT-DNA
Figure 13.
Surface Functionalization of CNT (or QD) with Oligonucleotide/Antibody (Ab), Forming aCNT-DNA (or -Ab) Probe and QD-DNA (or -Ab) Probe, and Subsequent Addition of Target Oligonucleotide (or Antigen) to Form a CNT-QD Assemblya (a The unbound QD probe was obtained by simple centrifugation separation, and the supernatant fluorescence intensity of QDs was monitored by spectrofluorometer. (System 1) Formation of CNT-QD hybrid in the presence of complementary DNA target. (System 2) Three-component CNT-QD system with the purpose to detect three different DNA targets simultaneously. (System 3) CNT-QD protein detection system based on antigen-antibody immunoreaction.) (With permission from American Chemical Society)
probes (50 μL of each, [CNT]) 1 mg/mL and [DNA]) 0.1 nM) and 3 QD-DNA probes (50 μL of each, [DNA] ) 0.1 nM) (as shown in Scheme 3, System 2). A mix of the six probes formed a uniform solution. Three cDNA targets were used in this system. Two microliters of each DNA target with different concentration was added into the six-probe mixture, individually. NoncDNA target (CGC GAT CTC AGG TCG ATA GG) was used as the control experiment. In addition, we mixed the three DNA targets (2 μL of each) to form a 6 μL DNA solution containing three different DNA targets, and then incubated the three DNA targets with the six-probe mixture simultaneously. The mixture solution containing probes and targets was incubated at 75 °C for 5 min and then at 25 °C for 20 min. After hybridization with target DNA molecules, CNT-QD assemblies were formed and then removed by centrifugation at 2000 rpm for 5 min. The unbound QD-DNA probes in supernatant PBS buffer were immediately detected by spectrofluorometer.
Figure 14.
Formation of CNT-QD assemblies for ultrasensitive DNA detection. (With permission from American Chemical Society)
5.10. CNT-QD system for antigen detection via antigenantibody immunoreaction (System 3)
To confirm that the CNT-QD system can be used for antigen detection, a novel CNT-QD immunoassay system based on antigen-antibody immunoreaction was established. BRCAA1 protein was chosen as a typical example. CNT-Ab and QD-Ab probes were prepared as shown in the Supporting Information. The immunoreactions procedure is described as follows: 50 μL of CNT-Ab probe ([CNT] ) 1.0 mg/mL, [Ab] ) 1.0 nM) was reacted with 10 μL of BRCAA1 antigen (concentration from 0 to 1.0 nM) for 30 min, then 50 μL of QD-Ab probe (1.0 nM) was added, and the mixture was incubated for 2 h at room temperature. After the immunoreaction, the sandwich-type immunocomplex was formed on the surface of CNT probes. The unbound QDs-pAb was obtained by simple centrifugation separation and the supernatant fluorescence intensity of QDs was monitored by spectrofluorometer (figure 14.).
5.11. Characterization of CNT-QD hybrids
TEM images were taken with a JEM 100-CXII microscope (JEOL, Japan) at 100 kV to demonstrate the formation of CNT-QD assembly. The zeta potentials of CNT-QD aqueous suspension were measured using a MALVERN ZETAZIZER 2000 instrument (U.K.). Fourier transform infrared (FT-IR) spectra were recorded using a PE Paragon 1000 spectrometer. UV–vis absorption spectra were obtained on UNICAM UV 300 spectrometers (Thermo Electronic). Raman measurements were carried out on a Jobin Yvon microRaman system (Ramanor U1000, Instruments SA, USA) using a Spectra-Physics Ar ion laser at an excitation wavelength of 514.5 nm (2.41 eV). All measurements were taken at room temperature, and for each sample the Raman data were collected at different light spots on the sample surface. For every Raman spectrum taken, the position of the peaks was verified by calibrating the spectral positions with respect to the silicon substrate peak seen at 521 cm-1.
6. Results and discussion
6.1. Formation of self-assembled CNT-QD nanocomplex
TEM images of QDs, CNTs, and CNT-QD nanocomplexes are shown in Fig. 15. The size distribution of CdSe nanoparticles is narrow with an average particle size of 3.0 nm in diameter, while the diameters of CNTs are about 20~30 nm. When the colloids of CdSe nanoparticles were mixed with CNT-COOHs or CNT-OHs in aqueous solution, very few CdSe nanoparticles can be bound onto the nanotubes (Fig. 15a, b). On the other hand, the TEM images give a direct view of the CdSe nanoparticles binding to CNT-NH2 (Fig. 15c); the CNTs with -NH2 surface groups are densely coated with CdSe nanoparticles. For all three CNT/QD systems no precipitation was observed for at least 4 weeks. For CNT-COOH, most of the CNTs remained suspended for over 1 year after adding CdSe QDs because of the strong electrostatic repulsion between CNTs and QDs, which is consistent with the TEM pictures as shown in Fig. 15.
Figure 15.
TEM images of (a) a mixture of CNT-COOH and CdSe, (b) a mixture of CNT-OH and CdSe, and (c) self-assembled CNT-NH2-CdSe nanocomplexes. Scales bar: 50 nm. (With permission from American Chemical Society)
To further investigate the interaction between CNTs and QDs, the -potential of the CNTs and CdSe samples are summarized in Fig. 16. Positively charged CNT-NH2, negatively charged CNT-COOH, and negatively charged QD-COOH can be confirmed from -potential analysis. Thus, addition of positively charged –NH2-modified CNTs to the negatively charged CdSe QDs with a COOH group results in formation of CNT-CdSe complexes by electrostatic interaction as shown in Scheme 1. The interaction forces between QDs and CNTs in aqueous solution are mainly electrostatic forces, which is why they are weakest for negative carboxyl-terminated CNTs. At pH 7.0, CdSe has a negative charge ( potential = 36 mV from Fig. 16); therefore, amino-terminated cationic CNTs ( potential = +32 mV) have the biggest impact on the QDs.
The UV-vis absorption spectra of CdSe nanocrystals (a) before adding CNT solution and (b) after adding CNT-COOH, (c) CNT-OH, and (d) CNT-NH2 are shown in Fig. 13A. The spectral feature located at ca. 580 nm has been assigned to the first excitonic transition occurring in CdSe nanocrystals. No obvious change of the band gap (580 nm) of the QDs is observed after adding CNT samples, indicating that no larger QD agglomerates were formed after adding CNT; in particular, for the CNT-NH2-QD samples, individual QD was separately coated on the CNT surface (Fig. 11c) instead of agglomerate.
Figure 16.
Zeta potentials of (a) mercaptoacetic acid coated CdSe, (b) CNT-COOH, (c) CNT-OH, and (d) CNT-NH2 in aqueous solution. (With permission from American Chemical Society)
6.2. Photoluminescence (PL) quenching of CdSe QDs in the presence of CNTs
The PL emission spectra (Fig. 17B-D) show that CdSe QDs have strong PL, and the wavelength of the PL maximum for CdSe was at 552 nm. The CNT concentration dependence of the PL intensity of CdSe QDs is shown in Fig. 17B-D. The decrease in the PL intensity was the most marked change in the PL spectrum observed upon addition of CNTs. For all types of CNTs, their increasing concentrations caused a linear reduction in the PL of CdSe. The effect was strongest for CNT-NH2 (Fig. 17D), less pronounced for CNT-OH (Fig. 17C), and weakest for CNT-COOH (Fig. 17B).
6.3. Calculation of quenching constants from the Stern-Volmer equation and double-logarithmic equation
where I0 and I are, respectively, PL intensities in the absence and presence of CNT, KSV is the Stern-Volmer dynamic quenching constant, and [CNT] is the concentration of CNT. The equation assumes a linear plot of I0/I versus [CNT], and the slope is equal to KSV. The Stern-Volmer constants express CdSe QD accessibility to the CNT. Fig. 18A shows Stern-Volmer quenching curves describing I0/I as a function of CNT concentration. The Stern-Volmer constants (KSV) for quenching of CdSe PL intensity by different kinds of CNTs are presented in Table 1. KSV were calculated from the plots shown in Fig. 18A.
Figure 17.
A) UV-vis spectra of QDs before and after adding CNTs; (B-D) PL spectra of CdSe QDs by adding CNTs. The concentration of CNT from top to bottom: 0, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 20.0, 30.0, 50.0, and 100.0 (mg/L). (With permission from American Chemical Society)
Figure 18.
A) Stern-Volmer plot and (B) double-logarithmic plot for CdSe PL quenching by CNT with different concentration. (With permission from American Chemical Society)
Alternatively, analyses of the PL data were performed using the double-logarithmic equation (Pan et al., 2006c). According to this approach, the QDs PL intensity scales with the CNT concentration ([CNT]) through the following equation:
I0I=1+KSV[CNT]E3
The binding constant Kb is obtained by plotting log[ (I0 - I)/ (I- Is)] versus log[CNT], where I0 and Is are the PL intensities of the QDs alone and the QDs saturated with the CNTs, respectively. The slope of the double-logarithm plot obtained from the experimental data is the number of n, whereas the value of log[CNT] at log[ (I0 - I)/ (I - Is)] ) 0 equals the logarithm of the dissociation constant Kdiss. The reciprocal of Kdiss is the quenching constant Kb. The PL intensity values (I) were obtained from the area under the PL spectra. Figure 4B represents the plot of log[ (I0 - I)/ (I - Is)] versus log[CNT] for CNT-COOH, CNT-OH, and CNT-NH2. Values of Kb and n obtained from Fig.18B are shown in Table 1.
sample
Stern-Volmer equation KSV[L/mg]
Double-logarithmicequation Kb(n) [L/mg]
CNT-COOH
0.060
0.0631(1.000)
CNT-OH
0.106
0.1365(1.434)
CNT-NH2
0.176
0.269(1.346)
Table 1.
Quenching Constants of CdSe by CNTs from the Stern-Volmer Equation and Double-Logarithmic Equation.
PL of CdSe QDs was strongly quenched by amino-terminated CNTs but only poorly quenched by CNT-COOH, as shown by KSV and Kb values. The data were well fitted by a straight line, typical of a simple dynamic quenching mechanism. For [CNT] >10 mg/L, KSV values increase rapidly for CNT-NH2 + QD solution, but fewer changes can be observed for CNT-OH + QD and CNT-COOH + QD solution. The data shown in Table 1 indicate that quenching constants of KSV and Kb from the Stern-Volmer equation and double-logarithmic equation are very similar. The quenching constant (KSV and Kb) for CNT-NH2 is much higher than that of CNT-COOH and CNT-OH, indicating CNT-NH2 is more strongly bound to the QD compared with CNT-COOH and CNT-OH. These observations support that surface groups and their charges are key factors of the CdSe-CNT interactions, which also confirm that PL properties of CdSe QDs are closely associated with their surface structure and surface charges. The -potential analysis in Fig. 16 showed that both carboxyl and amino surface groups are ionized in water, only hydroxyl-terminated CNTs bear no charge on their surface. Thus, the dynamic process quenched the emission of CdSe QDs because of the electrostatic interactions between the CNT surface and CdSe surface in aqueous solution.
From the results of TEM, zeta-potential analysis, PL quenching experiments, the Stern-Volmer equation, and the doublelogarithmic equation we can make conclusions that the quenching intensity of CdSe caused by CNT highly depends on the surface structure and charge of CNTs and can be ordered as follows: CNT-NH2 > CNT-OH > CNT-COOH.
6.4. Effect of pH on the quenching constants
Control experiments were carried out by adding the same buffer but without CNTs to QD solution; furthermore, buffer samples with pH ranging from 4.0 to 10.4 were used to investigate the pH effect on QD PL properties. As shown in Fig. 19A, little PL change can be observed for QDs after adding buffer solutions without CNT at pH values from 4.0 to 10.4, which highly suggest that PL quenching was mainly caused by the CNTs in the QD solution.
Figure 19.
A) pH effect of buffer solution on QD PL intensity. (B) Plot of quenching constant versus solution pH: (a) CNT-COOH, (b) CNT-OH, and (c) CNT-NH2. (With permission from American Chemical Society)
To investigate the pH effect on quenching constant (KSV and Kb), CNTs were added to QD solution at different pH environments, and then KSV and Kb were calculated from the Stern-Volmer equation and double-logarithmic equation. Plots of quenching constant versus pH value are given in Fig. 19B. In the CNT-NH2 case, the quenching constant (KSV and Kb) values decreased slightly with increasing pH from 4.0 to 10.4. It can be explained as follows. QD is negatively charged by the zeta potential result (Fig. 16). At lower pH, amino groups of CNT-NH2 are highly protonated, but at higher pH, the surface charge becomes less positive; thus, the electrostatic interactions between amino groups CNT-NH2 and the QD molecule are weakened, resulting in the decrease of quenching constants. For CNT-COOH and CNT-OH samples, little changes in KSV and Kb can be observed as shown in Fig. 19B.
6.5. FT-IR and raman spectroscopy
As shown in Fig. 20A (a), the FT-IR spectrum of the carboxylic-acid-coated QDs showed a strong asymmetric vibration characteristic of carboxyl groups at 1711 cm-1. The FT-IR spectra of QDs after adding CNT-COOH and CNT-OH are shown as Fig. 20A (b, c), indicating no shift of the -COOH peak at 1711 cm-1. The QD-COOH peak at 1711 cm-1 shifts to 1732 cm-1 after adding CNT-NH2, indicating a strong electrostatic interaction between CNT-NH2 and QDs. Raman spectra of CNT-NH2 before and after binding with QDs are shown in Fig. 20B. CNT-NH2 reveals two main peaks at 214 and 1582 cm-1 in the Raman spectrum. After attachment of QDs onto the sidewalls of CNT-NH2, the original G peak at 1582 cm-1 of the uncoated CNT-NH2 was shifted to 1578 cm-1 (Fig. 20B). However, adsorption of QD on CNT-NH2 did not result in any Raman shift at 214 cm-1, indicating that the assembly of QD nanocrystals onto the tubes deformed the sidewall of CNTs (Pan et al., 2006b). For CNT-COOH and CNT-OH, no Raman shift can be observed after addition of QDs (data not shown), indicating CNT-QD complexes cannot be formed by adding QD to the CNT-COOH or CNT-OH.
Figure 20.
A) FT-IR spectra of (a) QD-COOH, (b) CNT-COOH + QD, (c) CNT-OH + QD, and (d) CNT-NH2 + QD. (B) Raman spectra of CNT-NH2 and CNT-NH2 + QDs. (With permission from American Chemical Society)
6.6. The quenching mechanism between CdSe QDs and CNTs
With the linearity, the concentration of CNT can be monitored by measuring the PL intensity of CdSe QDs. PL quenching is a process which decreases the intensity of the QD PL emission. QD PL quenching may occur by several mechanisms (Fan and Jones Jr, 2006; Pan et al., 2006c): (1) dynamic quenching, (2) static quenching, (3) quenching by energy transfer, and (4) charge-transfer reactions. In this paper we can draw the conclusion that QD quenching in the presence of CNTs is mainly caused by dynamic quenching and energy-transfer mechanisms (Clapp et al., 2004; Oh et al., 2005). When quenching occurs by a dynamic mechanism, the quenching is an additional process that deactivates the excited state besides radiative emission (Cui et al., 2004b). The dependence of the QD emission intensity on CNT concentration is given by the Stern-Volmer equation and double-logarithmic equation. The accessibility of CNT to QD is reflected in the quenching constant (including KSV and Kb). For CNT-COOH and CNT-OH, low values of KSV and Kb indicate low exposure of CdSe to CNTs, i.e., the QD did not attach to the CNT surface. In particular, in the case of CNT-COOH, strong repulsion between CNT and QD in aqueous solution can be confirmed by the TEM image and zeta-potential data. The interparticle space between CNT and QD will be the main determinative factor for PL quenching according to the dynamic quenching and energy transfer mechanisms. According to energy-transfer mechanism, energy transfer is proportional to r-6 (r = distance between CNT and QD), i.e., PL will be severely quenched when QDs are getting close to the surface of the CNT. It is very difficult for QD-COOH to reach the CNT-COOH surface because of the strong repulsion electrostatic force. However, QD-COOH can easily bind to the CNT-NH2 surface to form CNT-QD nanocomplex, which can be confirmed by FT-IR and Raman spectroscopy, resulting in strong PL quenching of QDs; thus, the strong quenching of QDs caused by CNT-NH2 is mainly due to attachment of the QDs onto the CNT surface. These QDs have no shifted emissions at 552 nm (Fig. 17), indicating a relatively unchangeable size. Given that the CNTs quenched the CdSe QDs, the interaction between CNTs and QDs must provide an alternative, nonirradiative decay path (Clapp et al., 2004; Cui et al., 2004b; Oh et al., 2005). It is believed that this nonirradiative decay path occurs because the electron affinity between the CdSe QDs and the CNTs is sufficiently different that it allows electron transfer from the QDs to the CNTs (Clapp et al., 2004; Pan et al., 2006c). In other words, formation of CNT-QD conjugates favors electron transfer from the quantum dots (donor) to the CNTs (acceptor) such that the excited electrons are accepted by the CNTs rather than being emitted as the PL peak. For CNT-COOH and CNT-OH, QDs cannot reach the CNTs surface, so that energy transfer will be very difficult to occur for the CNT-COOH/QD or CNT-OH/QD system; therefore, a dynamic quenching mechanism will be fit for the CNT-COOH/QD or CNT-OH/QD system. For CNT-NH2/QD system, besides dynamic quenching, electron/energy transfer will be allowed from QDs to CNTs, i.e., an energy-transfer quenching mechanism.
6.7. CNT-QD assembly system for DNA target detection (System 1)
The supernatant fluorescence spectra in Fig. 21 show that the QD-DNA probe has strong fluorescence, and the maximum fluorescence wavelength for CdSe was at ∼570 nm. The cDNA target (sequence shown in figure 13, system 1) with concentration ranges of 0~200 pM was added to the mixture of CNT-DNA and QD-DNA probes to form CNT-QD nanohybrids. After centrifugation at 2000 rpm for 5 min, the CNT-QD nanohybrids were removed; therefore, only unbounded QD-DNA existed in the supernatant buffer. The decrease in the fluorescence intensity was the most marked change in the fluorescence spectrum observed upon addition of the cDNA target.
Control experiments were carried out by adding cDNA target to the QD-DNA solution in the absence of the CNT-DNA probe (Fig. 22A). In addition, buffer solutions with pH ranging from 4.0 to 11.0 were used to investigate the pH effect on the fluorescence properties of the QD-DNA probe (Fig. 22B). As shown in Figure 2A and B, little PL change can be observed for QD-DNA probes after adding complementary target DNA solutions without CNT-DNA probe at pH values from 4.0 to 11.0, which highly suggests that the QD fluorescence intensity was
Figure 21.
Fluorescence intensity of supernatant after adding target 1 with different concentrations (from top to bottom): 0, 10, 16, 25, 40, 50, 60, 75, 90, 120, 150, and 200 pM. (With permission from American Chemical Society)
Figure 22.
Plot of supernatant fluorescence intensity versus DNA concentration: (A) effect of cDNA target concentration on fluorescence intensity of QD-DNA probe; (B) fluorescence of QD-DNA probe at different pH values from 4.0 to 11.0; (C) noncDNA target with concentration from 0 to 200 pM incubated with CNT-DNA and QD-DNA probes (no fluorescence changes in supernatant); and (D) marked fluorescence decrease found for CNT-DNA/QD-DNA mixture solution after the incubation with cDNA target. (With permission from American Chemical Society)
stable in the presence of oligonucleotides at different pH values. From the plot of fluorescence intensity versus DNA concentration (Fig. 22D), their increasing concentrations caused a linear reduction in the fluorescence intensity of QDs at the target 1 concentration range of 0~200 pM. Furthermore, noncDNA target was used as a control, and after addition of noncDNA target, little fluorescence change can be observed (Fig. 22C), indicating no CNT-QD assembly can be formed in the presence of noncDNA. In this experiment, the available DNA detection range is from 0 to 200 pM, and according to the measurement results on the samples of gradually diluted DNA targets, the available good repeatability limit of detection is as low as ∼0.2 pM (Fig. 22D).
6.8. Simultaneous three DNA target detection based on CNT-QD assembly system (System 2)
Three QDs with different emission wavelengths at 510, 555, and 600 nm were used as probes to detect three target DNA molecules simultaneously (called QD510, QD555, and QD600 probes). Their spectrally resolved fluorescence signal is displayed in Fig. 23A. As can be seen, the fluorescence signal can be split into three bands with emission wavelengths of 510 (QD510), 555 (QD555), and 600 nm (QD600). Plotting the fluorescence intensities for the three wavelengths, that is, λ = 510, λ = 555, and λ = 600 nm, respectively, against the concentration of DNA targets results in the linear decrease of fluorescence intensity given in Fig. 23B-F. In Fig. 23B, we used a noncDNA target as control experiment, no fluorescence changes of the supernatant after incubation of noncDNA can be observed, indicating no CNT-QD hybrid formation in this system. By adding three cDNA targets all together to the CNT-DNA and QD-DNA probe solution (six-probe solution), we found a fluorescence decrease at the three wavelengths (510, 555, and 600 nm; Figure 23C), indicating CNT-QD hybrids formed in all the probes corresponding to three different wavelengths. In Figure 23D, only one cDNA target (sequence shown in Scheme 3, system 2, CNT-QD510) was incubated with the six-probe solution, and we found fluorescence decreased only at 510 nm, and no fluorescence changes were found at 555 and 600 nm, indicating there is only CNT-QD510 hybrid formed, but no CNT-QD555 and CNT-QD600 hybrids. Similar results can be seen from Fig. 23E and F. Only CNT-QD555 and CNT-QD600 hybrids were formed upon the addition of only corresponding cDNA target, respectively. Briefly, we demonstrated the CNT-QD system can be used as simultaneous multicomponent detection system with detection limits of ∼0.2 pM for each DNA target.
From Fig. 23C-F and Fig. 22D, we noticed that fluorescence intensity is 60 at [DNA] = 0, but on the other hand, fluorescence intensity was 0 at [DNA] = 225 pM. Although at the fluorescence intensity= 0, the amount of target DNA is 2 L × 225 pM = 4.5 × 10-16 mol. In the CNT-DNA probe and QD-DNA solution, [DNA] = 1.0 nM and volume = 50 μL, so the DNA amount in the CNT-DNA probe or QD-DNA probe solution is 50 μL × 0.1 nM = 5 × 10-15 mol. From Figure 22D, if we assumed all QD particles were binding to the CNT surface at the point of fluorescence intensity = 0, we concluded that the QD number should be equal to the DNA amount (i.e., 4.5 × 10-16), so the number of DNA molecules on each QD particle is: 5 × 10-15 mol/4.5 × 10-16 = 11. That is to say, there are 11 DNA molecules on each QD, and only 1 DNA molecule performed DNA hybridization to produce the CNT-QD hybrid. But we could not calculate the number of DNA probes on each CNT, since the lengths of the CNT were not known.
Figure 23.
Sensitivity and linearity analysis of the three-component CNT-QD DNA detection system by plotting fluorescence intensity against target DNA concentration. (A) QD probes with three different fluorescence wavelengths at 510, 555, and 600 nm; (B) plotting of supernatant fluorescence intensity against DNA concentration upon incubation of noncDNA target with CNT-DNA and QD-DNA probes; (C) fluorescence decreased at the three peaks 510, 555, and 600 nm upon the adding three cDNA targets to the six-probe solution; (D) only the 510 nm band decreased by adding the only DNA target corresponding to CNT-QD510 system; and (E, F) fluorescence decreases at 555 and 600 when cDNA corresponding to CNT-QD555 and CNT-QD600 system, respectively, is added. (With permission from American Chemical Society)
According to Fig. 22D and\n\t\t\t\t\tFig. 23C-F, the QD fluorescence intensity (F) scales with the DNA concentration [DNA] (pM) through the following:
I0−II−Is=[CNTKdiss]nE4
6.9. CNT-QD assembly for antigen detection via antigen-antibody (Ag-Ab) immunoreaction (System 3)
The purpose of the CNT-QD assembly system is to ultrasensitively detect target biomolecules, trying to confirm that the CNT-QD ultrasensitive detection system can be used not only for a cDNA target, but also for an antigen-antibody system. The formation of the CNT-QD hybrid via immunoreaction of an antigen with CNT-Ab and QD-Ab probes can be demonstrated from the fluorescence decrease of unbound QD fluorescence, as shown in Fig. 24. Similar to System 1, we used the QD particles with fluorescence emission wavelength at 570 nm. Upon incubating with target BRCAA1 antigen, CNT-QD hybrid was removed by a simple centrifugation step, and supernatant QD fluorescence intensity decreased linearly at an antigen concentration ranging from 0 to 1.0 nM (Fig. 24a). By measuring the gradually diluted samples, we found that we can obtain good repeatability results within the concentration scope of more than ∼0.01 nM, which is also located within the scope of linearity between the QDs fluorescence and BRCAA1 concentration. For the control experiment, in the absence of the target antigen, no fluorescence decrease was observed from Fig. 24b.
Figure 24.
CNT-QD assembly system for antigen detection. (a) Linear plot of fluorescence intensity in supernatant against antigen concentration and (b) control experiment were conducted in the absence target antigen. (With permission from American Chemical Society)
From Fig. 24, we noticed that the fluorescence intensity is 0 at the point of the target [Ag] = 1.1 nM; that is, the amount of antigen is 1.1 nM × 10 μL = 1.1 × 10-14 mol. In CNT-Ab and QD-Ab probes, the antibody amount is 1.0 nM × 50 μL = 5 × 10-14 mol. The antibody number on each QD particle is (5 × 10-14 mol) / (1.1 × 10-14 mol) = 4.5, so we concluded that 4.5 antibody molecules are on each QD particle, and only 1 antibody on QD bound to the antigen to form the CNT-QD hybrid.
According to Fig. 24, the QD fluorescence intensity (F) scales with the antigen concentration [Ag] (nM) through the following:
F=−26.7[DNA]+60E5
As a comparison, the ELISA detection method for BRCAA1 antigen was performed according to the testing procedure for antigen-antibody interaction. The novel CNT-QD hybrids method reported herein was compared with ELISA (Cui et al., 2004a; Medintz et al., 2005; Pan et al., 2006a). BRCAA1 antigen samples were especially selected to be used for the immunoassay using the ELISA methods, and the result was ∼0.5 nM. In the case of protein detection, BRCAA1 antigen with a concentration < 0.5 nM cannot be detected by using the ELISA methods. In the CNT-QD method, BRCAA1 antigen can be detected with good repeatability at ∼0.1 nM below the detection limit. The results suggest the excellent CNT-QD method has a higher sensitivity than ELISA. The protein detection limit is ∼0.1 ppm by using a Dot-Blot fluorescent staining method (Yamada et al., 2004). In the case of oligonucleotide detection, the detection limit of the colorimetric polynucleotide detection method (Elghanian et al., 1997) is the same as that of our method. Bio-Barcodes assays have been studied and work comparably well over the 20~700 nM target concentration range (Nam et al., 2002; Nam et al., 2003). Therefore, quantification and detection of DNA/protein can be performed with higher accuracy and sensitivity.
Regarding the potential mechanism, the CNT probe and QD probe can form the nanocomposites under the existence of the complementary target oligonucleotides; the distance between CNT and QD highly depends on the length of the oligonucleotides, as observed in experiments; CNTs can quench the fluorescence signal of QDs; and the dynamic quenching and PL resonance energy transfer between CNTs and QDs should be responsible for the phenomena. When the CNT-QD method is used to detect the DNA or antigen molecules, the ratio of the CNT and QD probes is very important. According to our experience, 1:1 is suitable for almost all detected samples. When the sample concentration is lower than the concentration of the CNT and QD probes, part CNT probe and QD probe will be redundant because the distance between uncomplementary CNT probe and QD probe is far more than 10 nm. Therefore, the quenching degree of the QD probe caused by the CNT probe is much less, and the quenching degree can be detected as a control group. By measuring these gradually diluted samples with known concentration, the dose-effects standard curve can be set up before the samples with unknown concentration are detected. Therefore, the redundant CNT probes do not affect the final result of the detected samples.
Regarding the specificity and efficiency of the CNT-QD method, as is known, oligonucleotide hybridization has been broadly used for genetic diagnosis, chip detection, and so on. Its specificity and hybridization efficiency have been confirmed. Therefore, in this work, we did not focus on investigating the specificity and efficiency of the CNT-QD method. Our further work will evaluate its specificity and dynamic efficiency based on one-, two-, and three-base-pair mismatch probes and various temperature conditions as well as large quantities of background genomic and oligonucleotide existence as nonselective matrix effects.
Regarding the stability of the CNT and QD probes, asODN modified CNTs can enhance markedly the water solubility and dispersability of CNTs as reported (Zheng et al., 2003). We have also observed that the CNT probe is very stable at room temperature in PBS buffer for several months. The asODN-modified CdTe QDs are also water-soluble and very stable. Their PL intensities do not change at room temperature and in a dark environment for almost 2 years, and are almost not affected by different pH values, as reported (Pan et al., 2008). Conversely, the PL intensity of unmodified QDs can be affected seriously by different pH values.
7. Conclusions and future prospects
In this section, we firstly designed a novel facile strategy to assemble CNTs and QDs in common aqueous solution based on a simple electrostatic interaction between amine-terminated CNTs and carboxyl-capped CdSe QDs. Individual QD was separately coated on the CNT surface to form CNT-QD nanocomplexes; as a result, PL of QDs can be quenched by CNT. Increasing of the CNT concentration led to the decrease of the PL intensity of QDs. The Stern-Volmer equation and doublelogarithmic equation were successfully used to calculate the quenching constant (KSV and Kb) of CNT/QD systems, indicating that the interparticle space is the main parameter that determines the PL quenching constant, which can be explained by dynamic quenching and energy-transfer quenching mechanisms. Furthermore, quenching constants (KSV and Kb) show little changes by varying pH values from 4.0 to 10.4, indicating a high quenching stability in different pH environments. Our results demonstrated that the dependent emissive properties of QDs coupled with CNTs opens a straightforward methodology for investigating the interaction between fluorescent molecules and other nanomaterials and further application in optical chemical sensors.
Based on the above novel facile strategy to assemble CNTs and QDs, we developed a novel and efficient DNA/protein detection method for biomolecules (such as oligonucleotide and antigen), that is based on DNA hybridization and antibody-antigen immunoreaction by using asODN-labeled CNTs and QDs as the molecular probes. The CNTs and QDs probes functionalized with alkyloligonucleotide can be assembled into a nanohybrid structure upon the addition of a target oligonucleotide. This strategy based on oligonucleotide hybridization assembly can be used to prepare multicomponent assembly materials comprising differently shaped oligonucleotide-functionalized nanomaterials. We have also developed a promising nanoscale CNTs probe and QDs probe for the direct, rapid, inexpensive, and sensitive detection and quantification of DNA/protein. Our probe combines the DNA hybridization and antigen-antibody interaction and is versatile and capable of simultaneous processing of multiple samples. The established method has great potential in applications such as ultrasensitive pathogen DNA or antigen or antibody detection, molecular imaging, and photoelectrical biosensors.
Based on the effects of MWNTs on the PL properties of CdSe QDs showed that CNTs could suppress the PL of QDs through both dynamic and energy transfer quenching mechanisms, a novel ultrasensitive DNA or antigen detection strategy by the CNT-QD assembly was designed for the direct, rapid, inexpensive, and sensitive detection and quantification of DNA/protein. Due to the DNA hybridization and antigen-antibody interaction and is versatile, simultaneous processing of multiple samples and higher assay throughput can be achieved. Nanotechnology provides a great opportunity to analytical chemists to develop better sensing strategies, but also relies on modern analytical techniques to pave its way to practical applications.
To sum up, nanomaterials are opening new horizons in the development of biosensor devices for simultaneous detection and measurement of specific multi-DNAs and antigens. Those biosensor devices could be useful for diagnosing and monitoring infectious diseases, monitoring the pharmokinetics of drugs, detecting cancer and disease biomarkers, analyzing breath, urine and blood for drugs of abuse, detecting biological and chemical warfare agents, and monitoring pathogens in food, among other conceivable applications. The unique and attractive properties of nanomaterials can markedly improve the sensitivity, selectivity, specificity and rapidity of biomolecules detection, offer the promising capability of detecting or manipulating atoms and molecules, and have great potential in the development of the miniaturizability or portability analytical system.
Acknowledgments
This work is supported by the National Key Basic Research Program (973 Project) (2010CB933901 and 2010CB93302), National 863 Hi-tech Project (2007AA022004), Important National Science & Technology Specific Projects (2009ZX10004-311), National Natural Scientific Fund No.20803040), Special project for nano-technology from Shanghai (No.1052nm04100), New Century Excellent Talent of Ministry of Education of China (NCET-08-0350), Shanghai Science and Technology Fund (10XD1406100) and Shanghai Jiao Tong University Innovation Fund for Postgraduates.
National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for thin film and microfabrication of Ministry of Education, Institute of Micro and Nano Science and Technology, Shanghai Jiao Tong University, Shanghai, P.R. China
National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for thin film and microfabrication of Ministry of Education, Institute of Micro and Nano Science and Technology, Shanghai Jiao Tong University, Shanghai, P.R. China
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1. Introduction
Wavelets [1] were born from the need to generate functions, especially those that present singularities, high gradients, discontinuities both in the time domain and in the frequency domain. Wavelets enable the high-resolution analysis of functions with these characteristics. An example of a problem that occurs when generating functions with a Fourier base is the Gibbs phenomenon. Such a phenomenon occurs because there is no way to represent functions that present discontinuities, even adding more elements in the base that will generate the function. A characteristic of wavelets is that they do not produce such an effect.
Wavelets are widely used in the solution of numerical problems in several areas of knowledge such as image compression, Numerical Harmonic Analysis [2], financial analysis, oil detection, differential Equations [3, 4], biomedical signals, analysis of electromagnetic integral Equations [5], optical fibers [6], among others. Many of these applications use the specific properties of wavelets, such as coefficients that are determined numerically, multi-resolution analysis to decompose a signal, integrals, and derivatives obtained numerically, energy concentrated in its compact and base with orthogonal elements.
2. Short introduction to wavelet theory
For the development of topics presented in this chapter, the reader must have as a prerequisite knowledge of functional analysis, linear algebra, measure theory and integration, differential and integral calculus. It is important to note that the wavelet basis is for the wavelet transform as well as the trigonometric basis is for the Fourier transform. Generally, the term wavelet is also used as a wavelet transform. The following subsections present these initial prerequisites to the reader.
2.1 Preliminaries on Hilbert spaces
In this subsection, some mathematical concepts necessary for a better formal understanding of the wavelet tool are defined. The definitions, contained in this section, are due to the author [2].
Definition 2.1 The space H is said to be a Hilbert space, if an inner product <,>, associated with a standard ‖=<,> has been defined in it. And a set of vectors vi, for i∈N an orthonormal system is said if the internal product <vn,vm>=δmn, for m,n∈N.
Definition 2.2 A set of vectors vn is orthonormal, if and only if, for every finite set of complex numbers xn, there is ∥∑nanxn∥2=∑an2, for n∈N.
Definition 2.3 In Hilbert’s H space, a set of vectors vn is said to be a Riez system, if there are constants 0≤c≤C<∞ such that for any finite set of complex numbers xn if you have:
c∑an2≤∥∑nanxx∥2≤C∑nan2E1
Definition 2.4 The space L2R is said to be an integrable square function space, that is,
L2R=f:R↦C:∫Rfx2dx<∞E2
For f,g∈L2R, define the inner product <f,g>=∫Rfxgx¯dx. On what, gx¯ is the complex conjugate of the function gx.
In particular ∥f∥=∥f∥2=∫Rfx2dx12, and f is said to be an integrable square.
Definition 2.5 Let f:R↦C be a function. The support of f, denoted by suppf, is the closing of the set x∈R:fx≠0. A function f is said to have compact support if the suppf set is compact.1
Definition 2.6 We say that a function f is generated by the basis functions f1…fn, if coefficients exist c1…cn such that:
f=∑i=1nficiE3
The concepts presented here about orthogonality and support of a f function, are fundamental to formalize the definition of wavelet. The following subsection presents the formal mathematical concept of wavelet.
2.2 Definition of wavelet
This subsection aims to define wavelet [2], the main mathematical tool used in the development of this chapter. However, it is necessary to define the expansion and translation of mathematical operations beforehand.
Definition 2.7 Givena>0, the expansion operator,Da, defined over afxfunction inL1orL2overR, is given by,Dafx=a12fx.
Definition 2.8 Givenb∈R, the translation operator,Tb, defined over a functionfx, inL1orL2overR, is given by,Tbfx=fxb.
Thus, using the expansion and translation operations defined above, a family of functions ψj,kx was built: L2→R, base orthogonal to L2R.
ψj,kxj,k∈Z=2j2ψ2jx−kj,k∈Z=D2jTkψxj,k∈ZE4
The Definition 2.9, uses the family of functions ψj,kxj,k∈Z, to define the term mathematically wavelet.
Definition 2.9 A function ψx is called wavelet if the collection ψj,kxj,k∈Z is an orthogonal basis on L2R. Where j and k are the resolution and translation of wavelet respectively.
By varying the values of j and/or k, it is possible to analyze with greater precision, for example, the behavior of functions that present abrupt changes in values and discontinuity. This type of analysis makes the wavelet a tool as or more efficient than the basic Fourier functions.
The definition 2.10 is another way used to define a wavelet.
Definition 2.10 A wavelet2 is a short duration wave, which has an average value equal to zero.
Due to the definition 2.10, wavelets resemble Fourier sine and cosine basis functions. Analogously to what is done in the Fourier transform, which has sine and cosine functions as base functions, in wavelet analysis, a function is decomposed into a base of wavelet functions.
The Fourier transform Fω expression of a ft function is given by (5):
Fω=∫−∞+∞fte−iωtdtE5
The expression (5) means that the Fourier transform is the sum of every ft sign multiplied by a complex exponential, which can be separated into cosine and sinusoidal components in the real and complex parts, respectively.
Similarly, the expression of the wavelet transform Wj,kf of a function ft, is given by (6):
Wj,kf=∫−∞+∞ftψj,ktdtE6
Similarly, the expression of the wavelet transform (6) is the internal product of the signal to be transformed by a wavelet function.
In the following subsection, among the most varied types of wavelets, the Daubechies wavelets are highlighted, which are the basis for the development of this chapter.
2.3 Daubechies wavelet properties
At 1988, a family of compact support wavelets [7] is built by Ingrid Daubechies. This family of wavelets has highly well-located elements. Each member wavelet is governed by a set of N integer coefficients and k=0.1…N−1 coefficients through scale relations (7) and (8). The ak and a1−k coefficients, which appear in the (7) and (8), are called filter coefficients and verify the following relations:
ϕx=∑k=0N−1akϕ2x−kE7
ψx=∑k=2−N1−1ka1−kϕ2x−kE8
In the Figures 1 and 2 below, we have the graphical representation of the Daubechies wavelet functions ϕ and ψ of kind 4.
Figure 1.
Daubechies wavelets ϕ. Source: This figure was generated by the author using the python programming language.
Figure 2.
Daubechies wavelet ψ. Source: This figure was generated by the author using the python programming language.
The functions ϕ in (7) and ψ in (8) are called the scale function ϕ and wavelet function ψ, respectively. The fundamental support of the scale function3 is the interval 0N−1 as the fundamental support of wavelet function ψx is the interval 1−N2N2. In the case of N=4, we have the graphs of the Figures 1 and 2.
To determine the filter coefficients ak and a1−k, which appear in the (7) and (8), we use the relations (9)–(12) below.
∑k=0N−1ak=2E9
∑k=0N−1akak−m=δ0,mE10
∑k=0N−1−1ka1−kak−2m=0E11
∑k=0N−1−1kkmak=0,m=0.1,…,N2−1,E12
where δk,m is the Kronecker Delta function.
3. Generating an analytical function of the type xk using wavelets
Analytical functions are those that can be locally around a point x0 expanded in a Taylor series, according to the following expression.
fx=∑n=0+∞fnx0n!x−x0nE13
In general according to the author [8], any fx function can be represented in terms of a wavelet base, as follows:
fx=∑m=−∞+∞ckϕx−m=∑m=−∞+∞cmϕmx.E14
The ck coefficients are called moments of the scale functions. In particular, for fx=xk, we have the expression (15), below:
xk=∑m=−∞+∞Mmk2jkϕ2jx−m,E15
Since Mmk the moment of the wavelet scales concerning the xk monomial, where k is the degree of the polynomial, m and j are the translation and resolution of the ϕ wavelet. The justification for the construction of the equation is found in the work of [8, 9, 10], in which the author concludes that the cmj coefficients for approximating a monomial of the xk form, using a Daubechies wavelet base ϕ, looks like this:
cmj=Mmk2jkE16
The justification used in the approximation (15) of a polynomial function of type fx=xk derives from the number of null moments,
∫−∞+∞xkψxdx=0,k=0.1,…,N2−1E17
According to the Eq. (17), the N Daubechies Wavelet has N2 vanish moments, being possible to represent a polynomial of degree at most N2−1, using the ϕx scale function. The polynomial approximation using the scale function is formalized in the following definition.
Definition 3.1 A wavelet has p vanish moments (18), if and only if, the wavelet scale function ϕ can generate polynomials of degree up to p−1 [Eq. (19)]. That is, the scale function alone can be used to represent these polynomials. The fact that it has more null moments means that the scale function can represent more complex functions.
∫−∞+∞xmψxdx=0;m=0,1,…,N2−1E18
fx=p1+p2x+…+pk−1xk,k≤N2−1E19
In general, a Daubechies wavelet of kind N, properly translated and adjusted to the appropriate resolution level, generates a polynomial of degree k, with the relation between N and k given by N=2k+2. For example, to generate a polynomial of degree 1 a wavelet of Daubechies of kind 4 is necessary.
To generate a polynomial with n+1 terms, in the function of Daubechies wavelets of genres 4,6,8,…,N−1, we use the momentum equation and the polynomial expansion as a function of wavelets.
where k is the degree of the polynomial j and m are the resolution and translation of the wavelet respectively.
In the next subsection, the calculation of the moment generating function, which appears in the expression 21 as a coefficient of xk, is shown in detail.
4. Moment generating function
The calculation of the moment generating function according to the author [11] is of fundamental importance to approximate the functions by wavelets. The deduction of the moment-generating function now begins. For this, the mathematical expression is used
Mmk=∫−∞+∞xkϕx−mdxE23
which refers to the moment of the wavelet scale ϕ in relation to the monomial xk.
From (35), (34), and (24), we get the moment generating function Mmk:W→R, where W is wavelet space, m is the translation of the scale function and k is the degree of the polynomial to be approximated.
The analytical expression for Mmk was developed during the author’s research [11] and to validate the results found, a comparative study was made with other numerical results [12, 13] of the scientific literature.
Similar to what was done with the calculation of the moments for the function ϕ, there is also the calculation of the moments for the function ψ. This is given by integral (38)
∫−∞+∞xmψxdx=0;m=0,1,…,N2−1.E38
The following is an example of the calculation of the moments for the case of Daubechies wavelets of a kind N=4.
Example 4.1 In this example, the Daubechies wavelet of kind 4 is used to generate the analytical polynomial function fx=x. According to the definition 3.1, the scale function of Daubechies of genus N=4, generates a line (polynomial of degree 1). To represent a 1 monomial with a 4 Daubechies wavelet in the 01 range, the translations ϕx,ϕx+1,ϕx+2, whose supports are 0.3,−1.2,−2.1, that is:
The support of the linear combination (39), represented in Figure 3, is obtained by the intersection of the supports of the translations of the function ϕx. This intersection results in the interval I=0.1. This fact defines well the function to be integrated in the I range. In Figure 3, the number of translations of the function ϕx to generate fx=x is illustrated.
Figure 3.
Translations required to represent the analytical function fx=x using Daubechies wavelets of kind 4. Source: Own authorship.
Figure 4 shows the graph of translated functions ϕx,ϕx+1 and ϕx+2 respectively, that form a base to generate the function fx=x.
Figure 4.
Translations required to represent the analytical function fx=x using Daubechies wavelets of kind 4. Source: This figure was generated by the author using the python programming language.
The calculation using the moment generating function depends on the Daubechies wavelet coefficients of kind 4. These coefficients are obtained by the Eqs. (9)–(12), which gives rise to the following non-linear system.
Replacing the value of m by m=−1, m=−2 and k=1, we obtain:
M−11=−0,366025400E44
M−21=−1,366025400E45
So, the representation for the x polynomial (for a resolution j=0) is:
x=0,634ϕx−0,366ϕx+1−1.366ϕx+2E46
In Figure 5, we have the graphical representation of the function obtained of the expression (46). Here the function fx=x is generated by linear combination of wavelets ϕx,ϕx+1 and ϕx+2.
Figure 5.
Function fx=x using Daubechies wavelets of kind 4. Source: This figure was generated by the author using the python programming language.
The representation for the expression (46) using the summation is given by,
x=∑m1=−20Mm112jkϕ2jx−m1E47
The expression for writing polynomials of degrees k=2 and k=3 in terms of Daubechies wavelets is given by
x2=∑m2=−42Mm222jkϕ2jx−m2E48
x3=∑m3=−60Mm332jkϕ2jx−m3E49
See that to generate the polynomials (48), (49) is necessary to use Daubechies wavelets of kind 6 and 8, according with the definition 3.1.
4.1 Taylor polynomial using Daubechies wavelets
The Taylor polynomial or Taylor series is an expression that allows the calculation of the local value of a function f using your derivatives. For this, the function f must be of class C infinite (represented by C∞) which implies that the f is infinitely derivable in an interval containing a point x0. The expression for the Taylor polynomial for the function f is as follows,
The expression (52) is another way of writing Taylor’s polynomial using Daubechies Wavelets.
Example 4.2 Consider the analytical function fx=ex, using Daubechies wavelet of kind a N=4 is possible to write this function f in terms of this wavelet. For this, Taylor’s series development around the point x0=0 of this function is given by:
ex=∑n=0+∞xnn!E53
Using only two summation terms in the expression (53), we have:
The expression (55) allows us to approximate the exponential function using a base of Daubechies wavelets. This type of approximation, although simple for this case, is very useful in the case of representation for functions other types.
In the following example, the expression (46) is used to approximate Taylor’s series developments for the functions sx=ex,fx=coshx,gx=sinhx and hx=ln1+x.
Example 4.3 For the functions fx=coshx,gx=cosx and hx=secx. Taylor’s series development of these functions around the point x0=0 is:
coshx=∑n=0+∞x2n2n!E56
sinhx=∑n=0+∞x2n+12n+1!E57
ln1+x=∑n=1+∞−1n+1xnnE58
In order to verify the potentiality of the application of Daubechies wavelets we will calculate the value of the functions in (53), (56), (57) and (58) evaluated at point x = 1. Considering only 7 terms in each summation. For obtain the results using Daubechies wavelets we apply the expression (55) in each summation (53), (56), (57) and (58). In the Table 1 we have a comparison between the calculation of the values of the functions sx=ex,fx=coshx,gx=sinhx and hx=ln1+x evaluated at point x=1, using the Taylor series and the Daubechies wavelets of kind 4.
Function
Value in x=1, Taylor Series4
Value in x=1, Daubechies Wavelets5
Error %
sx=ex
2.716666667
2.716735469443329
0.0025%
fx=coshx
1.543088161791753
1.543058311287478
0.0019%
gx=sinhx
1.1750199840127897
1.1750591521108822
0.00376%
hx=ln1+x
0.6456349203122008
0.6456349190214307
1,9.10−7%
Table 1.
Comparison of the values obtained by the Taylor series and by Daubechies wavelets.
Calculation using Taylor Series.
Calculation using Daubechies wavelets of kind 4.
Table 1 appears here only as a way of showing the quality of the approximations using the Daubechies wavelets of kind 4. Obviously if we want more precise values, we must use Daubechies wavelets of the kind greater than 4. This will cause changes in the resolution and translation of each wavelet, but the result will be even better.
5. Conclusions
Daubechies wavelets are quite versatile mathematical tools. They can be used to analyze, generate, decompose a function, or even a signal that is represented by an analytical function. This type of application is widely used, for example, in electrical engineering in studies of magnetic fields and electric fields. The theory exposed in this chapter provides tools to carry out these studies. The use of the Taylor series as a way of approximating analytical functions is a very used technique in applied mathematics. Making use of the Taylor series with wavelets is another option to perform an approximation of analytical functions. In future work, we are researching other wavelets, for example Deslauries-Dubuc interpolets, that have an even better approach quality. As Deslauriers-Dubuc interpolets and others in research.
Acknowledgments
The author would like to thank UFERSA for support during my doctoral studies.
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Wavelet analysis can be applied to practical problems that require a high degree of precision, for example, in the study and analysis of electromagnetic propagation in optical fibers, solutions of differential equations involving engineering problems, in the transmission of WiFi signals, in the treatment and analysis of biomedical images, detection of oil sources through the study of seismic signals.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73428",risUrl:"/chapter/ris/73428",signatures:"Paulo César Linhares da Silva",book:{id:"10065",type:"book",title:"Wavelet Theory",subtitle:null,fullTitle:"Wavelet Theory",slug:"wavelet-theory",publishedDate:"February 24th 2021",bookSignature:"Somayeh Mohammady",coverURL:"https://cdn.intechopen.com/books/images_new/10065.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83881-948-4",printIsbn:"978-1-83881-947-7",pdfIsbn:"978-1-83881-955-2",isAvailableForWebshopOrdering:!0,editors:[{id:"109280",title:"Dr.",name:"Somayeh",middleName:null,surname:"Mohammady",slug:"somayeh-mohammady",fullName:"Somayeh Mohammady"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"329037",title:"Prof.",name:"Paulo",middleName:null,surname:"Silva",fullName:"Paulo Silva",slug:"paulo-silva",email:"paulo258@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Short introduction to wavelet theory",level:"1"},{id:"sec_2_2",title:"2.1 Preliminaries on Hilbert spaces",level:"2"},{id:"sec_3_2",title:"2.2 Definition of wavelet",level:"2"},{id:"sec_4_2",title:"2.3 Daubechies wavelet properties",level:"2"},{id:"sec_6",title:"3. Generating an analytical function of the type xk using wavelets",level:"1"},{id:"sec_7",title:"4. Moment generating function",level:"1"},{id:"sec_7_2",title:"4.1 Taylor polynomial using Daubechies wavelets",level:"2"},{id:"sec_9",title:"5. Conclusions",level:"1"},{id:"sec_10",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Daubechies I. Recent results in wavelets applications. Journal of Electronic Imaging, 1998, 7, 719–724'},{id:"B2",body:'Walnut D. An Introduction to Wavelet Analysis. Applied and Numerical Harmonic Analysis. Birkha user Boston, 2002'},{id:"B3",body:'Bertoluzza S. A wavelet collocation method for the numerical solution of partial differential equations. Applied and Computational Harmonic Analysis,3, 1–9, 1996'},{id:"B4",body:'Choudhury A. Wavelet method for numerical solution of parabolic equations. Journal of Computational Engineering, 2014, 2014, 1–12, 2014. https://doi.org/10.1155/2014/346731'},{id:"B5",body:'Robert L, Weng C. A study of wavelets for the solution of electromagnetic integral equations. IEEE Transactions on antennas and propagation, 1995, 43, 802–810'},{id:"B6",body:'Silva P, Melo R, Silva J. Optical Fiber Coupler Analysis Using Daubechies Wavelets. Journal of Microwaves, Optoelectronics and Electromagnetic Applications (JMOe), 2020, 19(3), AoP 294-300. https://doi.org/10.1590/2179-10742020v19i3825'},{id:"B7",body:'Ingrid Daubechies. Ten lectures on wavelets. 1992, Society for Industrial and Applied Mathematics, USA'},{id:"B8",body:'Burgos R. Análise de Estruturas Utilizando Wavelets de Daubechies e Interpolets de Deslauriers-Dubuc. PhD thesis, Pontifícia Universidade Católica, PUC, Setembro 2009'},{id:"B9",body:'Burgos R. Finite elements based on deslauriers-dubuc wavelets for wave propagation problems, Applied Mathematics, 2016, 7, pp. 1490–1497'},{id:"B10",body:'Burgos R. Solution of 1d and 2d poisson’s equation by using wavelet scaling functions. Thermal Engineering, 2016, 15, pp. 68–75'},{id:"B11",body:'Silva P, Silva J, Garcia A. Daubechies wavelets as basis functions for the vectorial beam propagation method, Journal of Electromagnetic Waves and Applications, 2019, 33:8, 1027-1041, DOI: 10.1080/09205071.2019.1587319'},{id:"B12",body:'Gopinath R and Burrus C. On the moments of the scaling function psi. Departament of Electrical and Computer Engineering-IEEE, 1992, 963–966'},{id:"B13",body:'Butzer P, Fischer A, Ruckforth K. Scaling functions and wavelets with vanishing moments. Computers Math. Applic, 1994, 27, 33–39'}],footnotes:[{id:"fn1",explanation:"A set is said to be compact if it is limited and closed."},{id:"fn2",explanation:"Anglophone term to designate a small wave, in the sense of having a fast duration."},{id:"fn3",explanation:"We emphasize that the scale function has energy concentrated in its support that is determined by the genus of the wavelet, that is, suppϕ=0N−1, and that the total energy of the scale function is unitary, that is, ∫−∞+∞ϕdx=1."}],contributors:[{corresp:"yes",contributorFullName:"Paulo César Linhares da Silva",address:"linhares@ufersa.edu.br",affiliation:'
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IntechOpen chapters and articles are distributed under CC BY 3.0 licences allowing users to “copy, use, distribute, transmit and display the work publicly and to make and distribute derivative works, in any digital medium for any responsible purpose, subject to proper attribution of authorship...” and there is no non-commercial restriction.
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Here the word “asymmetric” means that the available information of the follower is some sub-\n\nσ\n\n-algebra of that available to the leader, though they play as different roles in the classical literatures. Stackelberg equilibrium is represented by the stochastic versions of Pontryagin’s maximum principle and verification theorem with partial information. A linear-quadratic (LQ) leader-follower stochastic differential game with asymmetric information is studied as applications. If some system of Riccati equations is solvable, the Stackelberg equilibrium admits a state feedback representation.",book:{id:"6756",slug:"game-theory-applications-in-logistics-and-economy",title:"Game Theory",fullTitle:"Game Theory - Applications in Logistics and Economy"},signatures:"Jingtao Shi",authors:[{id:"147959",title:"Dr.",name:"Jingtao",middleName:null,surname:"Shi",slug:"jingtao-shi",fullName:"Jingtao Shi"}]},{id:"62516",title:"The Game Theory: Applications in the Wireless Networks",slug:"the-game-theory-applications-in-the-wireless-networks",totalDownloads:1460,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Recent years have witnessed a lot of applications in the computer science, especially in the area of the wireless networks. The applications can be divided into the following two main categories: applications in the network performance and those in the energy efficiency. The game theory is widely used to regulate the behavior of the users; therefore, the cooperation among the nodes can be achieved and the network performance can be improved when the game theory is utilized. On the other hand, the game theory is also adopted to control the media access control protocol or routing protocol; therefore, the energy exhaust owing to the data collision and long route can be reduced and the energy efficiency can be improved greatly. In this chapter, the applications in the network performance and the energy efficiency are reviewed. The state of the art in the applications of the game theory in wireless networks is pointed out. Finally, the future research direction of the game theory in the energy harvesting wireless sensor network is presented.",book:{id:"6756",slug:"game-theory-applications-in-logistics-and-economy",title:"Game Theory",fullTitle:"Game Theory - Applications in Logistics and Economy"},signatures:"Deyu Lin, Quan Wang and Pengfei Yang",authors:[{id:"258432",title:"Dr.",name:"Deyu",middleName:null,surname:"Lin",slug:"deyu-lin",fullName:"Deyu Lin"},{id:"259049",title:"Prof.",name:"Quan",middleName:null,surname:"Wang",slug:"quan-wang",fullName:"Quan Wang"},{id:"261098",title:"Dr.",name:"Pengfei",middleName:null,surname:"Yang",slug:"pengfei-yang",fullName:"Pengfei Yang"}]},{id:"63373",title:"Infinite Supermodularity and Preferences",slug:"infinite-supermodularity-and-preferences",totalDownloads:997,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This chapter studies the ordinal content of supermodularity on lattices. This chapter is a generalization of the famous study of binary relations over finite Boolean algebras obtained by Wong, Yao and Lingras. We study the implications of various types of supermodularity for preferences over finite lattices. We prove that preferences on a finite lattice merely respecting the lattice order cannot disentangle these usual economic assumptions of supermodularity and infinite supermodularity. More precisely, the existence of a supermodular representation is equivalent to the existence of an infinitely supermodular representation. In addition, the strict increasingness of a complete preorder on a finite lattice is equivalent to the existence of a strictly increasing and infinitely supermodular representation. For wide classes of binary relations, the ordinal contents of quasisupermodularity, supermodularity and infinite supermodularity are exactly the same. In the end, we extend our results from finite lattices to infinite lattices.",book:{id:"6756",slug:"game-theory-applications-in-logistics-and-economy",title:"Game Theory",fullTitle:"Game Theory - Applications in Logistics and Economy"},signatures:"Alain Chateauneuf, Vassili Vergopoulos and Jianbo Zhang",authors:[{id:"248905",title:"Prof.",name:"Jianbo",middleName:null,surname:"Zhang",slug:"jianbo-zhang",fullName:"Jianbo Zhang"},{id:"248908",title:"Prof.",name:"Alain",middleName:null,surname:"Chateauneuf",slug:"alain-chateauneuf",fullName:"Alain Chateauneuf"},{id:"248910",title:"Dr.",name:"Vassili",middleName:null,surname:"Vergopoulos",slug:"vassili-vergopoulos",fullName:"Vassili Vergopoulos"}]},{id:"60809",title:"Game Theory Application in Smart Energy Logistics and Economy",slug:"game-theory-application-in-smart-energy-logistics-and-economy",totalDownloads:1025,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In many parts of the world, energy sectors are transformed from conventional to the smart deregulated market structures. In such smart deregulated market environment, cooperative game theory can play a vital role for analyzing various smart deregulated market problems. As an optimization tool, cooperative game theory is very useful in smart energy logistics and economy analysis problem. The economy associated with smart deregulated structure can be better optimized and allocated with the help of cooperative game theory. Initially, due to regulated structure, there is no cooperation between different entities of energy sector. But after new market structure, all the entities are free to take their own decisions as an independent entity. Transmission open access of energy logistics is also comes into the picture, as all the generators and demands have the same right to access the transmission system. In this market situation, multiple utilities are using the same energy logistic network. This situation can be formulated as a cooperative game in which generators and demands are represented by players. This chapter deals with energy logistic cost allocation problems for a smart deregulated energy market. It is cooperative in nature as all the agents are using the same energy logistic network.",book:{id:"6756",slug:"game-theory-applications-in-logistics-and-economy",title:"Game Theory",fullTitle:"Game Theory - Applications in Logistics and Economy"},signatures:"Baseem Khan",authors:[{id:"240063",title:"Dr.",name:"Baseem",middleName:null,surname:"Khan",slug:"baseem-khan",fullName:"Baseem Khan"}]}],onlineFirstChaptersFilter:{topicId:"75",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:18,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:1,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. 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Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:754,paginationItems:[{id:"310674",title:"Dr.",name:"Pravin",middleName:null,surname:"Kendrekar",slug:"pravin-kendrekar",fullName:"Pravin Kendrekar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/310674/images/system/310674.jpg",biography:"Dr. Pravin Kendrekar, MSc, MBA, Ph.D., is currently a visiting scientist at the Lipid Nanostructure Laboratory, University of Central Lancashire, England. He previously worked as a post-doctoral fellow at the Ben-Gurion University of Negev, Israel; University of the Free State, South Africa; and Central University of Technology Bloemfontein, South Africa. He obtained his Ph.D. in Organic Chemistry from Nagaoka University of Technology, Japan. He has published more than seventy-four journal articles and attended several national and international conferences as speaker and chair. Dr. Kendrekar has received many international awards. He has several funded projects, namely, anti-malaria drug development, MRSA, and SARS-CoV-2 activity of curcumin and its formulations. He has filed four patents in collaboration with the University of Central Lancashire and Mayo Clinic Infectious Diseases. His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. Dr. Adimule has attended, chaired, and presented papers at national and international conferences. He is a guest editor for Topics in Catalysis and other journals. He is also an editorial board member, life member, and associate member for many international societies and research institutions. His research interests include nanoelectronics, material chemistry, artificial intelligence, sensors and actuators, bio-nanomaterials, and medicinal chemistry.",institutionString:"Angadi Institute of Technology and Management",institution:null},{id:"284317",title:"Prof.",name:"Kantharaju",middleName:null,surname:"Kamanna",slug:"kantharaju-kamanna",fullName:"Kantharaju Kamanna",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284317/images/21050_n.jpg",biography:"Prof. K. Kantharaju has received Bachelor of science (PCM), master of science (Organic Chemistry) and Doctor of Philosophy in Chemistry from Bangalore University. He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. 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:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{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. 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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. 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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. 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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. 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Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. 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International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. 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This topic is dedicated to biologically plausible descriptions and computational models - at various abstraction levels - of neurons and neural systems. This includes, but is not limited to: single-neuron modeling, sensory processing, motor control, memory, and synaptic plasticity, attention, identification, categorization, discrimination, learning, development, axonal patterning, guidance, neural architecture, behaviors, and dynamics of networks, cognition and the neuroscientific basis of consciousness. Particularly interesting are models of various types of more compound functions and abilities, various and more general fundamental principles (e.g., regarding architecture, organization, learning, development, etc.) found at various spatial and temporal levels.",annualVolume:11419,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"13818",title:"Dr.",name:"Asim",middleName:null,surname:"Bhatti",fullName:"Asim Bhatti",profilePictureURL:"https://mts.intechopen.com/storage/users/13818/images/system/13818.jpg",institutionString:null,institution:{name:"Deakin University",institutionURL:null,country:{name:"Australia"}}},{id:"151889",title:"Dr.",name:"Joao Luis Garcia",middleName:null,surname:"Rosa",fullName:"Joao Luis Garcia Rosa",profilePictureURL:"https://mts.intechopen.com/storage/users/151889/images/4861_n.jpg",institutionString:null,institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",fullName:"Yalcin Isler",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",institutionURL:null,country:{name:"Turkey"}}}]},{id:"24",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",annualVolume:11420,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"1177",title:"Prof.",name:"António",middleName:"J. R.",surname:"José Ribeiro Neves",fullName:"António José Ribeiro Neves",profilePictureURL:"https://mts.intechopen.com/storage/users/1177/images/system/1177.jpg",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"220565",title:"Dr.",name:"Jucheng",middleName:null,surname:"Yang",fullName:"Jucheng Yang",profilePictureURL:"https://mts.intechopen.com/storage/users/220565/images/5988_n.jpg",institutionString:null,institution:{name:"Tianjin University of Technology",institutionURL:null,country:{name:"China"}}},{id:"29299",title:"Prof.",name:"Serestina",middleName:null,surname:"Viriri",fullName:"Serestina Viriri",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYOalQAG/Profile_Picture_1620817405517",institutionString:null,institution:{name:"University of KwaZulu-Natal",institutionURL:null,country:{name:"South Africa"}}},{id:"315933",title:"Dr.",name:"Yalın",middleName:null,surname:"Baştanlar",fullName:"Yalın Baştanlar",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002qpr7hQAA/Profile_Picture_1621430127547",institutionString:null,institution:{name:"Izmir Institute of Technology",institutionURL:null,country:{name:"Turkey"}}}]},{id:"25",title:"Evolutionary Computation",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization",scope:"Evolutionary computing is a paradigm that has grown dramatically in recent years. This group of bio-inspired metaheuristics solves multiple optimization problems by applying the metaphor of natural selection. It so far has solved problems such as resource allocation, routing, schedule planning, and engineering design. Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",annualVolume:11421,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"111683",title:"Prof.",name:"Elmer P.",middleName:"P.",surname:"Dadios",fullName:"Elmer P. Dadios",profilePictureURL:"https://mts.intechopen.com/storage/users/111683/images/system/111683.jpg",institutionString:"De La Salle University",institution:{name:"De La Salle University",institutionURL:null,country:{name:"Philippines"}}},{id:"106873",title:"Prof.",name:"Hongwei",middleName:null,surname:"Ge",fullName:"Hongwei Ge",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Dalian University of Technology",institutionURL:null,country:{name:"China"}}},{id:"171056",title:"Dr.",name:"Sotirios",middleName:null,surname:"Goudos",fullName:"Sotirios Goudos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9IuQAK/Profile_Picture_1622623673666",institutionString:null,institution:{name:"Aristotle University of Thessaloniki",institutionURL:null,country:{name:"Greece"}}},{id:"15895",title:"Assistant Prof.",name:"Takashi",middleName:null,surname:"Kuremoto",fullName:"Takashi Kuremoto",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLrqQAG/Profile_Picture_1625656196038",institutionString:null,institution:{name:"Nippon Institute of Technology",institutionURL:null,country:{name:"Japan"}}},{id:"125844",title:"Prof.",name:"Wellington",middleName:"Pinheiro Dos",surname:"Santos",fullName:"Wellington Santos",profilePictureURL:"https://mts.intechopen.com/storage/users/125844/images/4878_n.jpg",institutionString:null,institution:{name:"Federal University of Pernambuco",institutionURL:null,country:{name:"Brazil"}}}]},{id:"26",title:"Machine Learning and Data Mining",keywords:"Intelligent Systems, Machine Learning, Data Science, Data Mining, Artificial Intelligence",scope:"The scope of machine learning and data mining is immense and is growing every day. It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",annualVolume:11422,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"43680",title:"Prof.",name:"Ciza",middleName:null,surname:"Thomas",fullName:"Ciza Thomas",profilePictureURL:"https://mts.intechopen.com/storage/users/43680/images/system/43680.jpeg",institutionString:null,institution:{name:"Government of Kerala",institutionURL:null,country:{name:"India"}}},{id:"16614",title:"Prof.",name:"Juan Ignacio",middleName:null,surname:"Guerrero Alonso",fullName:"Juan Ignacio Guerrero Alonso",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6HB8QAM/Profile_Picture_1627901127555",institutionString:null,institution:{name:"University of Seville",institutionURL:null,country:{name:"Spain"}}},{id:"3095",title:"Prof.",name:"Kenji",middleName:null,surname:"Suzuki",fullName:"Kenji Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/3095/images/1592_n.jpg",institutionString:null,institution:{name:"University of Chicago",institutionURL:null,country:{name:"United States of America"}}},{id:"214067",title:"Dr.",name:"W. David",middleName:null,surname:"Pan",fullName:"W. David Pan",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSEI9QAO/Profile_Picture_1623656213532",institutionString:null,institution:{name:"University of Alabama in Huntsville",institutionURL:null,country:{name:"United States of America"}}},{id:"72920",title:"Prof.",name:"Yves",middleName:"Philippe",surname:"Rybarczyk",fullName:"Yves Rybarczyk",profilePictureURL:"https://mts.intechopen.com/storage/users/72920/images/system/72920.jpeg",institutionString:"Dalarna University, Faculty of Data and Information Sciences",institution:{name:"Dalarna University",institutionURL:null,country:{name:"Sweden"}}}]},{id:"27",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",annualVolume:11423,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"275140",title:"Dr.",name:"Dinh Hoa",middleName:null,surname:"Nguyen",fullName:"Dinh Hoa Nguyen",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRbnKQAS/Profile_Picture_1622204093453",institutionString:null,institution:{name:"Kyushu University",institutionURL:null,country:{name:"Japan"}}},{id:"20259",title:"Dr.",name:"Hongbin",middleName:null,surname:"Ma",fullName:"Hongbin Ma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRhDJQA0/Profile_Picture_2022-05-02T08:25:21.jpg",institutionString:null,institution:{name:"Beijing Institute of Technology",institutionURL:null,country:{name:"China"}}},{id:"28640",title:"Prof.",name:"Yasushi",middleName:null,surname:"Kambayashi",fullName:"Yasushi Kambayashi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYOQxQAO/Profile_Picture_1625660525470",institutionString:null,institution:{name:"Nippon Institute of Technology",institutionURL:null,country:{name:"Japan"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/17072",hash:"",query:{},params:{id:"17072"},fullPath:"/chapters/17072",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)}()