\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"1917",leadTitle:null,fullTitle:"Principal Component Analysis",title:"Principal Component Analysis",subtitle:null,reviewType:"peer-reviewed",abstract:"This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. 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",isbn:"978-1-80356-573-6",printIsbn:"978-1-80356-572-9",pdfIsbn:"978-1-80356-574-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"92c25420000e8d708ce07782c747cba5",bookSignature:"Dr. Kyeong Kang and Ms. Fatuma Namisango",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11914.jpg",keywords:"E-service, Digital Service, Digital Communication, Digital Process, Online Process, Communication, Innovation Culture, Social Computing, Collaborative System Design, E-service Innovation, Information Systems, User Experience Design",numberOfDownloads:4,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 11th 2022",dateEndSecondStepPublish:"May 13th 2022",dateEndThirdStepPublish:"July 12th 2022",dateEndFourthStepPublish:"September 30th 2022",dateEndFifthStepPublish:"November 29th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"An internationally renowned academic, Dr. Kang complete her Ph.D. in Computing Sciences from the University of Technology, Sydney. She was a visiting scholar at the Georgia Institute of Technology, US, and Yonsei University, Korea. Her research focuses on e-commerce, e-service, digital service platforms, and human–technology innovation in diverse cultures.",coeditorOneBiosketch:"Dr. Namisango received her Ph.D. in Information Systems at the University of Technology Sydney. Her research spans cross-cultural issues in information systems, social information systems, sociomateriality and new ways of working and living, and human information behaviors. She published her work in Computers in Human Behavior, Information Systems Frontiers, and others.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"2114",title:"Dr.",name:"Kyeong",middleName:null,surname:"Kang",slug:"kyeong-kang",fullName:"Kyeong Kang",profilePictureURL:"https://mts.intechopen.com/storage/users/2114/images/system/2114.jpg",biography:'Dr. Kyeong Kang is an academic in Australia. She received a Ph.D. in Computing Sciences from the University of Technology, Sydney, and her research focuses on e-commerce, e-service, digital service platforms, and human–technology innovation in diverse cultures. As a leading researcher, she completed projects for the Australian Information Industry Association (AIIS) and the Department of Foreign Affairs and Trade (DFAT), Australia. She is currently conducting a project for the Asian Productivity Organization (APO). Her research contributions are well-known internationally. As a visiting scholar at the Georgia Institute of Technology, US (2012), Dr. Kang engaged in an ICT international development project. 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Her research focuses on cross-cultural issues in information systems, human factors in computing, social information systems, technology affordances, sociomateriality, and new ways of working and living.\r\nDr. Namisango received a Master’s Degree in Information Technology from Makerere University, Uganda. She received a Ph.D. in Information Systems at the University of Technology Sydney (UTS), Australia, working under the supervision of Dr. Kyeong Kang and Prof. Ghassan Beydoun. She won the International Research Scholarship (IRS) and the UTS Presidents’ Scholarship to pursue her Ph.D. studies. 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It is actively investigated today due to the wide available bandwidth for very high data rate up to 480Mb/s and low power service over short distances in 10m range. According to FCC (Federal Communications Commission), the frequency spectrum allocated for UWB is 3.1-10.6 GHz, and the spectrum shape of modulated output power and maximum power level are limited to -41.3dBm/MHz, which ensures that UWB can coexist with existing spectrum users like GSM(Global System of Mobile communication), WLAN(Wireless Local Area Network) and Bluetooth.
Based on MB-OFDM(Multi-Band Orthogonal Frequency Division Multiplexing), WiMedia released the initial version of Physical Layer (PHY) Specification in September 2005. In this proposal, the UWB frequency spectrum from 3.1 GHz to 10.6 GHz is divided into 14 channels with 528MHz for each channel. These sub-bands are grouped into five band groups. It is seen that by increasing the signal bandwidth significantly, ultra-wideband achieves a high channel capacity and becomes an attractive solution to the ever-increasing data rate demands in wireless personal area networks (WPAN). In December 2005, European Computer Manufacturer\'s Association (ECMA) proposed the standard ECMA 368/369 on high-speed UWB physics layer and media access control layer based on MB-OFDM scheme. This has pushed the industrialization of UWB technology to a new stage again.
In China, UWB technology has also become a hot topic according to the issue of the UWB standard by Chinese Government in 2008. A new UWB scheme named dual carrier-orthogonal frequency division multiplexing (DC-OFDM ) has been proposed and applied in China. In China standard, only the band from 6.2GHz to 9.4GHz and the band from 4.2GHz to 4.8GHz are available for UWB applications. These bands are partitioned into 14 sub-bands of 264MHz bandwidth which means the bandwidth is halved in China’s DC-OFDM standard compared with the ECMA 368/369 standard. Thus the sampling frequency of the DACs(Digital-to-Analog Converter) and ADCs(Analog-to-Digital Converter) are halved too. The power consumption of the system can be reduced greatly. Moreover, in DC-OFDM UWB, two bands locating around two different carriers are utilized at the same time to form a bandwidth of 528 MHz for maintaining high-speed communication. In this way, the spectrum usage is more flexible and the spectrum efficiency is enhanced. However, the requirements of less than 9-ns hopping time of the carrier frequency as well as simultaneous dual-carrier outputs challenge the design of dual-carrier frequency synthesizer. Fig.1 shows the frequency spectrum for WiMedia and China UWB standard.
A fully integrated transceiver for DC-OFDM UWB system in the 6-9GHz band is present in this chapter. This chapter will describe the realization of a DC-OFDM UWB transceiver covering 6-9GHz bands in a low cost 0.13um CMOS process. Firstly, the RF receiver design will be described in section 2. Section 3 and 4 introduce respectively the designs of the RF transmitter and the 9-bands frequency synthesizer. The detailed measurement results are demonstrated in section 5, which is followed by the conclusions in section 6.
Frequency spectrum for WiMedia and China UWB standard
Fig.2 shows a block diagram of the proposed UWB receiver. Signals are received and filtered by the off-chip antenna and the RF(Radio Frequency) filter firstly. And then the received signals are amplified and converted to IF(Inter-media Frequency) baseband signal by RF front-end building blocks. After further filtering and amplifying, the analog baseband signals should be large enough to drive the ADC for digital signal processing. The receiver\'s local oscillator (LO) should be a fast-hopping frequency synthesizer that generates carrier tones according to the band plan in Fig.1. Performances such as in-band phase noise and reference spur are specified as -80 dBc and -40dBc respectively, which are not so stringent. And the I/Q mismatch is designed as 2.5 degree and 0.2 dB.
Normally the noise figure of channel select filter is around 30 dB, thus the conversion gain of RF front-end building blocks should be larger than 30 dB to suppress the noise from LPF(Low Pass Filter). But in that case, the linearity of the receiver will get worse. In order to improve the linearity of the receiver, the conversion gain of the RF front-end building blocks is set to be around 24 dB(average) with variable gain of 12 dB. The NF(Noise Figure) of the LPF is designed to be less than 18 dB to guarantee low noise of the receiver. The LNA(Low Noise Amplifier) utilizes a fully differential structure and presents an input matching to 50ohm for the off-chip antenna. It should provide a maximum gain of 18 dB to suppress noise from mixer and baseband circuits. As LNA sets the baseline for the noise figure of the receiver, the NF of the LNA should be optimized to lower than 5 dB. Following is a quadrature mixer with a fixed gain of 6 dB. The 5th-order Chebyshev type band-selection LPF is implemented after the mixer. Unlike normal channel select filter, the proposed LPF should provide a maximum gain of 30 dB, with a NF less than 18 dB at maximum gain mode.
According to the Friis Equation, the noise of the LPF nearly doesn\'t contribute to the total input referred noise of the receiver, leading to a very low noise figure. As the back-end block of the receiver, the filter tackles with slightly large signals, leading to stringent linearity requirement for the filter. Since the filter suppresses adjacent channel interferers to some extent, the linearity of the filter is proportionally improved. Sharp rejection of out-of-band signal is also required. Considering the difference between the sub-band\'s bandwidth of two standards, the cut-off frequency of the filter is switchable between 264 MHz and 132 MHz. Finally, the PGA(Programmable Gain Amplifier) amplifies the signal from the LPF and delivers constant-magnitude signals to the ADC.
Architecture of the proposed receiver
Attaining an input impedance match for the wide band receiver is particularly difficult because parasitic may dominant the input impedance network. Fig.3 gives a presentation of the LNA for the proposed UWB receiver. A resistive shunt feedback topology is adopted in the LNA design, which achieves a wideband matching with a good balance between area cost and performances. Although there is a slight degradation of the noise figure comparing to other techniques like LC ladder (Bevilacqua A. et al., 2004) and transformer feedback matching (Shin D. H., et al., 2007), quite a large number of inductor coils are avoided. Bonding wire inductance Lbonding and the ESD(Electro-Static Discharge) capacitance together with the PAD capacitance Cpad are co-designed with other on-chip components. The load stage is an R-L-C tank. The load inductor LL can be replaced by a differential inductor to get a smaller area. However, we split it into two symmetrical inductors for convenience of cascading with mixer in the layout. A fully differential topology is utilized in LNA design to have the input impedance match independent of the bonding wire inductance from the source of M1 to ground. Fig.4 shows the simulated S11 with different bonding wire inductance.
Schematic Diagram of Low Noise Amplifier
Simulated S11 with different bonding wire inductance
Fig.5 shows the folded quadarture down- conversion mixer for the UWB receiver. A fully differential Gilbert-cell based structure with I/Q branches sharing the same RF input stage is implemented in the mixer, which eliminates the mismatch present in down conversion topology with separate I/Q mixers. Exploring merged architecture (Sjöland H, et al., 2003) for the quadrature mixer can also minimize the capacitive load to the LNA. Compared with the traditional structure of mixer, the folded structure utilized in this work separates the input stage and switching stage. Thus different bias current can be applied to the input stage and switching stage, better performances are achieved. The bias current of the input stage is bigger to guarantee good performance on conversion gain and noise figure. On the contrary, small current in the switching stage can lower the 1/f noise and dc-offset, which is significantly important in zero-IF receivers.
Quadrature down conversion mixer circuit
The main difference between the two standards is that the intermediate frequency is 4.125MHz-264MHz and 1MHz-132MHz for WiMedia MB-OFDM and China UWB standard respectively. In order to support both standards, the cut-off frequency of the band-select filter should be switchable between 132MHz and 264MHz. Using two different filters to support each standard may be a possible solution, but will sacrifice a lot of die area. Furthermore, as the first stage of IF stage, the NF and linearity of the filter should be optimized. Thus the LPF should provide variable gain to suppress noise substantially at maximum gain mode and meet the linearity requirement when set as minimum gain. In this work, a fifth-order Chebyshev type programmable Gm-C filter is implemented.
The fifth order low pass filter is realized by a cascade of a first order RC filter and two biquads. The proposed architectures of the low pass filter and the biquad are illustrated in Fig.6. Note that the down-conversion mixer’s load resistors are utilized to form the first passive RC filter stage. As a result, simulations covering both the mixer and the filters should be taken to make sure that the overall frequency response and gain are optimized. The modified Nauta Gm cell (as shown in Fig.7) is implemented as the OTA(Operational Transconductance Amplifier) in the filter. The transconductances of the all the OTA are controlled by the digital data.
Structure of the Low Pass Filter
Modified Nauta OTA
The topology of the PGA (Programmable Gain Amplifier) is based on a source degenerated structure as illustrated in Fig.8. A switched resistor array is implemented to achieve variable gain from 0dB to 18dB with 2dB/step. High-gain amplification easily causes the following stages into saturation due to DC-offset and DC offset also leads to second-order harmonic distortion (HD2) of the received signals, resulting in SNR(Signal-to-Noise Ratio) degradation. Thus the DC-offset cancellation circuits are also included in the PGA design. The amplitude response of the PGA is designed to be flatness within the frequency range of 264MHz.
Topology of the PGA
The proposed transmitter utilizes the direct conversion architecture for its easiness of integration and low cost. As shown in Fig.9, it consists of a dual-mode I/Q LPF with mode-switch circuits, an I/Q up-conversion mixer with high-linear voltage-to-current (V2I) units, a two-stage power driver amplifier (PA). Besides, the trans-impedance amplifiers (TIAs) are integrated to measure the AC transfer character of the LPF.
The main signal flow of this transmitter is as follows. The ABB(Analog Baseband) voltage signals from the DACs are applied at the inputs of the I/Q LPF. With the correct mode-switch bit as well as the Digital Control Capacitor Array (DCCA) control word, the image signals of the DACs and the unwanted high frequency spurs are all filtered out in both 264-MHz and 132-MHz modes. After the output voltages of the LPF are converted into ABB currents by V2I units, they are up-converted into RF voltages by the switches in the up-conversion mixer at the rate of LO. Lastly, the differential RF voltages are amplified by PA(Power Amplifier) and are converted into single-ended one via the 6-9 GHz off-chip balun, to drive the antenna.
Block diagram of the proposed transmitter
The main requirements of this LPF are the attenuation of the out-band signals, the in-band ripple, the dual-mode operation with accurate cut-off frequency controlling and accommodation to the large input ABB voltages. According to the sampling rate of a common UWB DAC, the LPF should have an attenuation of about 45 dB from 264/132 MHz to 600/300 MHz at 264/132-MHz mode. Moreover, an in-band ripple of 0.5 dB is required. To obtain comparably good phase linearity, the 5th-order Chebyshev gm-c LPF is proposed. Besides, to deal with the ABB voltage as large as 300mVpp, the passive sub-filter is placed as the 1st-stage and the high-Q biquad is as the last stage. Also, to improve the linearity of the LPF under low supply voltage with low power, the trans-conductors are built with the Nauta’s structure (Nauta B, et al., 1992).
Architecture of the 5th-order Chebyshev LPF with mode-switch circuits
The simplified I-path schematic of the up-mixer is shown in Fig.11. It utilizes two double balanced Gilbert cells with their outputs summed to realize single-sideband (SSB) up-mixing. Since the I/Q up-mixer acts as I/Q modulator and up-conversion mixer in direct conversion transmitter, the performances of the transmitter are mainly determined by this circuit.
Low spurs, high linearity and wide bandwidth are the main challenges for the design of this up-conversion mixer. The main spurs in the output spectrum of the transmitter are the LO leakage and the sideband signal. The power of the LO leakage is determined by the offset of the I/Q ABB path. In order to reduce the power of LO leakage, an AC coupling is utilized between the V2I unit and the switches of the up-mixer as shown in Fig. 11. Besides, the linearity of the up-mixer is mainly affected by the V2I unit while the impact of the switch stage is of less importance (Zheng Renliang, et al., 2009). Many techniques ( Willy Sansen, 2006) have been proposed to improve the linearity of the V2I unit. Although the complete OPAMP-assisted V2I possess better linearity, its application is restricted by the power consumption to achieve sufficient GBW(Gain Bandwidth) of the OPAMP for UWB ABB as well as the limited voltage swing because of the low supply voltage. Instead, the simple OPAMP-assisted V2I unit is preferred. As shown in Fig.11, the V2I unit consists of the input PMOS transistor M1, the source degeneration resistor R1, the current-mirror transistor M2, M3, the AC coupling capacitor CB as well as the bias resistor for eliminating the DC-offset in V2I. The feedback loop is composed of M1, R1, M2, I1 and I2, where M2 acts as the simple single-transistor OPAMP. When applied at the gate of M1, the input ABB voltage is directed transferred to the terminals of R1, because any voltage changes at the gate will be transferred to the source of M1 to maintain a fixed VGS as required by the current source I1 and I2. Thus the input voltage is converted linearly into its current counterpart with a gain of 1/R1. The converted current ∆i circulates in M2. Then it is mirrored into the up-mixer by M3. A 400-Ω R1 is used to improve the linearity at the cost of the gain loss in V2I. To compensate it, a 6-dB gain is set at the current mirror. Furthermore, a broadband operation of the mixer is achieved by employing a differential inductor Ld to peak with the parasitic capacitance Cpar and two series resistors Rs to reduce Q of the overall load network.
Simplified I-path schematic of the up-mixer and its wideband load network
Since the PA is the last stage of the transmitting chain, its linearity determines the output IP3(Input 3rd order Intercept Point) of the transmitter according to the Friis’ formula. Moreover, the PA should possess sufficient gain to boost the output power of the up-mixer as well as to reduce the impact of former stages on the linearity of the transmitter. A flat gain of the PA is desired, too. Besides, considerations of the rejection to common-mode interferences should be taken because the tail current sources are eliminated to fit the low supply voltage.
As shown in Fig.12, the 1st stage of the PA is a combination of source follower (M1) and common source (M2) amplifier (Chang-Wan Kim, et al., 2005). The phase shift of the signal passing through the two amplifiers is 0°and 180°respectively. When the input signal Vin is applied at the two amplifiers, the common-mode signals in Vin become out-of-phase and their amplitudes are subtracted at node X/Y while the differential-mode signals in Vin become in-phase and their amplitudes are added at node X/Y. In this configuration, the input differential signals are amplified with the common-mode signals rejected. Therefore, the 1st stage increases the common-mode rejection ratio (CMRR) of the transmitter. In order to obtain a high CMRR, the gain of the two appliers, i.e. the source follower and the common source amplifier, should be equal. The transistors M1 and M2 have the identical size. Under this condition the ideal CMRR is infinite and the differential voltage gain is 6 dB. However, the post simulation of this circuit indicates that the CMRR is improved by 12 dB and the differential voltage gain is about +2 dB because the inherent unbalances between the two amplifiers. Moreover, as the impact of the parasitic capacitors the gain drops at high frequency.
The 2nd stage of the PA amplifies the RF signals to drive the off-chip balun. As the main amplification stage in this PA, its gain and linearity are important. Thus a class-A common source amplifier (Ma) is employed. A differential inductor (LPA) with center tap is used as the load of this stage to resonate with the capacitance including the parasitic capacitance of Mc as well as the PAD. Because the effective 50-Ω input resistors of balun-2 are part of the load network, its Q value is low and the gain is relatively flat. The value of the LPA is optimized according to the PAD capacitance Cpad and the bonding inductance Lb to ensure the peak of the gain is around 9 GHz instead of the middle of 6-9 GHz. Thus it compensates the gain drop of the 1st stage at high frequency. Besides, in this PA the cascode transistors, i.e. M3 and Mc, ease the Miller Effect to reduce the effective loading capacitance to the former stage and avoid the breakdown of the transistors during large signal period. 2-bit digital signals are used to select the required bias voltage for Ma; an 8-dB variable gain is realized.
Simplified schematic of two-stage PA
According to the band partition for UWB communication system shown in Fig.1, the SSB mixer-based generator for the frequency generations from group2 to group5 is proposed in Fig.13. It is based on the band generation plan (shown in Fig.14), which is designed with the objective of attaining a synthesizer solution that uses a minimum number of components while reducing the generation of spurs.
A PLL(Phase-Locked Loop) with quadrature voltage-controlled oscillator (QVCO) and an external reference of 48 MHz is implemented to generate 8448 MHz I/Q outputs as the fundamental LO frequency. The 8448 MHz in-phase and quadrature phase (I/Q) signals are applied to the quadrature SSB (QSSB) mixer to mix with another input whose frequency is switchable. These switchable input frequencies for QSSB mixer can be derived either from divided-by-2 dividers’ output or from a combination of a SSB mixer1 and a divided-by-2 divider. The final output phase accuracy largely depends on the quadrature input signals of the QSSB-mixer. Since divided-by-2 dividers are used to produce I/Q signals for some synthesized frequencies for high phase accuracy. The divider’s phase sequence and spectral purity may impact the mixer’s phase accuracy. A double balanced quadrature-input divided-by-2 (DBQID) frequency divider is implemented to suppress the third harmonic with high precise quadrature phase sequence.
Two frequency multiplexers are used to choose the right internal frequency for each channel. The band selection is accomplished by switching the capacitor bank of the QSSB mixers to the desired frequency and simultaneously switching its input to the desired frequency and phase. Fast switching can be achieved since they operate simultaneously. To suppress the sidebands caused by nonlinearity and mismatch at the output, the number of SSB mixers has been minimized. The synthesizer’s output frequencies are given as ffs_out=8448+/-264*m where m=0,1,2,3. and ffs_out=8448-264*n where n=4,5,6,7,8. The I/Q vectors of the internal frequencies travel through different traces and inevitably suffer from phase and gain mismatches when they reach the QSSB mixers. A Clock buffer is inserted before the QSSB mixer to calibrate the phase and gain mismatches of the input signals coming from different paths.
Architecture of the proposed frequency synthesizer
Frequency plan of the proposed frequency synthesizer
The QVCO is the most important circuit in a PLL and its phase noise greatly determines the overall PLL output noise performance. Quadrature coupling transistors in parallel quadrature voltage-controlled oscillator (P-QVCO) make a large contribution to the phase noise. A cascode structure can greatly reduce the noise from the cascode device. Better phase noise performance can be achieved by series connection between coupling and switching transistors (Andreani P, et al., 2007).
In the case of P-QVCO, through changing the ratio of the width of coupling transistor to the width of switching transistor, phase noise and phase error can be deal with for each other. The phase error cannot be improved by increasing phase noise for the series quadrature voltage-controlled oscillator (S-QVCO). The phase error of S-QVCO depends on the amount better phase error but worse phase noise performance. To suppress the sideband caused by phase error, top-series QVCO(TS-QVCO) is adopted to generate quadrature LO signal. The width ratio of coupling transistor to switching transistor is 1/2.
As shown in Fig. 15, a linearization technique is used to lower effective KVCO whereas maintain a same tuning range (Kuo C, et al., 2006). By employing this linearization, nearly the whole supply voltage range can be exploited. The varactors biased at different voltages connect with metal-insulator-metal capacitors in series as dc blockers. The dc bias voltages are generated by a resistor ladder. The resonators are both made of a differential inductor, an array of 7 bits two binary weighted switched capacitors and thick oxide MOS varactors. The tuning voltage ranges from 0 to 1.2 V. A small KVCO=60 MHz/V is adopted to achieve low AM-FM noise conversion. To filter the flicker noise from the tail current transistors, a large MOS capacitor is used at the gate of the current mirror.
Schematic of TS-QVCO
The multiplexers (MUX) are based on several differential pairs sharing a common resistance load. Their activation or deactivation is through a signal enable or disable the tail current. The port leakage and third harmonic rejection are the key issues. If the unselected input frequency is leaked at the output, it will generate unwanted center frequency, which poisons the output frequency even more than those frequencies not at the center of the bands. For the third MUX, it has as many as six inputs. Thus the port leakage must be solved. There are several methods to suppress the port leakage such as cascode structure. But it is not well suited in a low voltage application.
In this design, a couple of dummy transistors are added to a conventional current-steering MUX to eliminate the unwanted coupling. Fig.16 shows the circuit of the in-phase path of MUX1. Take transistors M1 and M5 for illustration, their input signals are same, but their drains are connected to the opposite output nodes. When Vin1I is not selected and M5 is omitted, Vin1I will couple to the output through the parasitic capacitance of M1. But with the presence of M5, Vin1I will couple to the opposite output node as well. The common response will be suppressed by the differential circuit. Therefore, good isolation is achieved between different inputs. The dummy input pairs consume no extra power. The tail current source of the dummy pairs is zero and the gate of the corresponding transistor is connected to the ground. Fig.17 shows the output spectra of multiplexers with and without the dummy input pairs. The two circuits are simulated with the same operation frequency and power consumption. It shows a port leakage suppression of 46dB better with the dummy input pairs than without them.
Schematic of the multiplexer
Simulation result of the MUX with and without the dummy pairs
The output of the MUX is feed to a latter SSB mixer, which functions as up or down conversion according to the input phase sequence. A common way to select up or down conversion is to add a controllable in-phase/opposite-phase buffer before the SSB mixer. In this design, the phase changing is merged in the MUX. As shown in Fig.16, the part in the left of the line is a replication of the right part, excepting the output which is connected to the opposite side. Hence, S0 and S1 change the phase sequences of Vin1, which helps to change between up and down conversion. As a result, such a configuration minimizes the hardware requirement, power consumption, and undesired sidebands.
Phase and gain mismatch at the input would cause image signal at the output of the SSB mixers. In order to counterbalance these mismatch, an IQ calibration module is interpolated before the second SSB mixer. Fig.18 shows the circuit of this module. Two identical buffers driven by the in-phase (0°) and quadrature-phase (180°) signals produce two outputs, normally equal to 45° and 135°, respectively. Here, one of the outputs is adjustable, while the other is fixed. These two outputs are quadrature in ideal. If th,
An interesting phenomenon using this method could be found. That is, the phase mismatch at the input will be transformed into the gain mismatch at the output; and the gain mismatch at the input will be transformed into the phase mismatch at the output. This could be illustrated by Fig.19, wherein In,i and In,q represent the input in-phase and quadrature-phase signal, Out,i and Out,q represent the output in-phase and quadrature-phase signal. It can be easily seen that the phase and amplitude error will be transformed into each other at the end. Hence
Schematic of the IQ calibration
Illustration of the phase and gain mismatch translation
The proposed 6-9GHz DC-OFDM UWB transceiver is fabricated in the 0.13-μm RF CMOS process. Fig.20 shows the die microphotograph. Total employed area including pads is 3.6 mm×4.5 mm.
In order to support both WiMedia MB-OFDM and China UWB standard, the receiver’s intermediate frequency is required to be switchable between 4.125-264MHz and 1-132MHz. Fig.21 shows the frequency response of the receiver for two standards with LO signal at 7.656GHz. The receiver achieves variable gain of 18dB to 80dB with 2dB/step for both standards. Gain flatness of less than 3dB is normally achieved. The spectrum mask of the transmitter with 40MHz QPSK modulation signal is shown in Fig.22. The integrated power is calculated at -10dBm.
The measured tuning range of QVCO is from 7.6 to 9.5 GHz. With an external reference of 48MHz, the synthesizer is locked at 8448 MHz with a phase noise is –90 dBc/Hz at 100 kHz offset and –105 dBc/Hz at 1 MHz offset as shown in Fig. 23. A normalized phase noise floor of -226.7 dBc/Hz is achieved. The measured integrated phase noise from 10 kHz to 50 MHz is 1.93˚. The reference spur level is -72 dBc seen from the Agilent E4440 spectrum analyzer. Experimental results reveal that the worst-case sideband occurs at 6336 MHz, which is about -30 dBc without I/Q calibration, as shown in Fig. 24, and is suppressed to below -45 dBc when the calibration is on. The band switching behavior is shown in Fig. 25. The bands are switched periodically and the synthesizer output is monitored. The longest switching time approximately equals to 1.4 ns, much less than 9.5 ns which is specified in dual-carrier MB-OFDM UWB systems.
Die microphotograph of Dual-band 6-9GHz Transceiver
Receiver frequency response, (left) At cut-off frequency of 264MHz; (right) At cut-off frequency of 132MHz
Spectrum mask of the transmitter with 40MHz QPSK modulation signal
Measured PLL phase noise and integrated phase noise
Output spectrum of 6336 MHz (up) without calibration and (down) with calibration
Measured band switching behaviour
A 0.13um CMOS 6-9GHz 9 Band Double-Carrier OFDM Transceiver for Ultra Wideband Application is proposed, which consists of a dual-carrier frequency synthesizer, two 6-9 GHz receiver cores, two 6-9 GHz transmitter cores and the digital control logical circuits, and so on. Only one PLL and two single-sideband mixer have been used in this dual-carrier frequency synthesizer. The VCO in the PLL oscillates at 8448 MHz. All the desired frequencies are generated by mixing the output of the divider chain with that of the VCO. Two carries can be output simultaneously with independent frequency setting and totally 9 carrier frequencies from 6336 MHz to 8712 MHz with a frequency gap of 264 MHz are available. The receiver core is composed of a 6-9 GHz LNA, IQ down-conversion mixer, LPF as well as VGA. The total gain of the receiver chain has been achieved about 80 dB with a minimum NF of 4.5 dB. The transmitter core consists of the IQ LPF, IQ up-conversion mixer and the power driver amplifier. The output IP3 of the transmitter is around +10 dBm and the output 1-dB compression point is about +0 dBm, which reveal high linearity of the transmitter. Besides, the power of LO leakage and sideband signal are less than -35 dB respect to the power of the desired RF signal. The chip size of the DC-OFDM transceiver is 3.6 mm×4.5 mm including the ESD-PADs. The power consumption is around 400mA from 1.2-V supply. The future research will focus on performance optimization, power consumption, and so on.
It is widely known that plants, like all the other living organisms, are colonized by a multitude of microorganisms [1]. Microbial communities are generally described from the composition (abundance and diversity of the populations that establish the community) and function (behavior and metabolic activity) points of view [2]. The extremely complex functions of microbial communities are not yet significantly understood, even though numerous studies were conducted on the composition of microbial communities [3]. Being modulated by abiotic and biotic factors, predation, competition, and cooperation interactions take place between the members of microbial communities. The environmental effects generated by the microbial activity have the ability of altering the aforementioned interactions furthermore [4].
Microorganisms that have a close relationship with the host plant, regardless of the environmental variables, form the core microbiome [5]. This core microbiome comprises keystone microorganisms that possess genes that can improve the fitness of the holobiont, which were selectively chosen from an evolutionary point of view [6]. Bacterial strains belonging to the families
On the other side of the spectrum, microorganisms found in a lower abundance, which are deeply influenced by the geographical location and habitat characteristics, represent the satellite taxa [8]. Despite their low abundance, satellite microorganisms have critical roles, such as protecting the plant against pathogens through the emission of volatile compounds with antifungal properties [9].
Although not long ago, the focus of plant-microorganisms interactions has been pathogenicity [10]; recently, the ability of some plant-associated microorganisms to directly or indirectly improve plant fitness and performance has been described, as well as the potential of using microbes as a replacement for some synthetic phytosanitary products [1]. A better grasp of the concept of the holobiont can lead to a better future of viticulture, as biocontrol, biofertilization, and biostimulation are realistic and achievable options to reduce the impact of biotic and abiotic stressors, as well as the use of chemical pesticides and fertilizers [11].
The aim of this study is to illustrate the impact of ceasing pesticide use for one year in vineyards on phyllosphere microbiota and soil fungi.
The term “microbiota” refers to the ensemble of microorganisms that exist in a defined environment [12]. In the vineyard ecosystem, endogenous factors, such as the plant age and cultivar, or exogenous factors, such as the cultivation system, geographic location of the plantation, farming techniques, seasonality, human intervention, soil characteristics, and surrounding plants, among others, can influence the presence of grapevine associated microorganisms to a certain extent [13]. It has been pointed out that the effects produced by the composition of the vineyard microbial communities may affect the phytosanitary status of the grapevine, and therefore wine quality, in a significant way [14]. The unique combination of bacteria, fungi, and other microscopic organisms that are found in association with the grapevine and the vineyard soil has even been termed “microbial terroir,” as it was found in the recent years to imprint distinctive traits on wine [15]. Zoochory, hydrochory, anemochory, and anthropochory are the known microorganisms dispersal methods that transfer microbiota from the surrounding environment to the grapevine or from one grapevine to another [16]. Perennial plant structures, such as canes, spurs, and bark harbor a greater, more stable microbial diversity, being at the same time one of the sources for the microbiota of the ephemeral structures − leaves, flowers, and berries [17].
Like many other eukaryotes, the grapevine is colonized by a multitude of microorganisms that play a certain role in its growth and survival. First used in 1991 [18], the term “holobiont” evolved to describe a host and the microbial community associated with it [19]. The holobiont concept states that, as it is the case for the animal kingdom, the plant’s health state is deeply influenced by the composition of its microbiota [16]. Depending on their role in relation to the plant, microbial species may be beneficial, pathogenic, or neutral [4].
Microorganisms may be found on the surface of grapevine organs, composing the epiphytic microbiota [20], or they may reside inside the plant tissues, making up the endophytic microbiota [21]. Some microbial species can be found both outside the plant structures and inside their tissues [16], meaning that microorganisms find gateways in piercing wounds caused by insects, stomata, or intercellular junctions, among others [22].
The sum of aerial plant organ surfaces represents the phyllosphere [23]. Depending on the plant organ they populate, phyllosphere microorganisms can be a part of the microbiota of the following plant compartments: leaves − phylloplane; flowers − anthosphere; fruits − carposphere; and stems − caulosphere. The phyllosphere is an open system colonized by complex microbial communities, even though the habitat can be considered hostile, as it is exposed to temperature oscillations, UV radiation, and plant-secreted antimicrobial compounds, as well as low water and nutrient accessibility [24]. The phyllosphere is dominated by the phylloplane, represented by the photosynthetically active foliar surface [23]. Considering the fact that the grapevine is a woody perennial plant that sheds leaves each autumn, the phylloplane is an ephemeral environment [24].
From a nutrient perspective, the foliar ecosystem is oligotrophic, due mainly to the presence of the hydrophobic cuticle that prevents plant metabolites from leaching and reduces water evaporation [25]. However, the presence of stomata, hydathodes, veins, and trichomes can assure a better nutrient supply for microorganisms [24]. Due to the distribution of such structures at the foliar level, the abaxial and adaxial sides of the leaf are colonized by different microorganisms [26]. In most cases, bacterial and fungal cells are found forming aggregates, held together by extracellular polymeric substances that can prevent desiccation [24]. Some phyllosphere inhabitants have also been found to protect their host plants through the substances they produce, that act like pesticides, stimulators, or fertilizers [27].
The other more intensely studied ephemeral grapevine organ is the fruit, the interest shifting often in this case from the health state of the plant to the impact on wine-making [16].
The soil is an everchanging complex environment, dominated by microbial activity [28]. As they have an important role in the cycle of nutrients and the decomposition of organic matter, microorganisms are a decisive factor in determining soil quality. A wide range of organisms coexists in the soil, including bacteria, fungi, archaea, viruses, oomycetes, protists, and arthropods, which are involved in complex trophic networks [29]. Bacteria and fungi are the dominant taxonomic groups, accounting for approximately 90% of the microbiota found in soil samples [30]. Bacteria are the most abundant soil microorganisms and are the first to react and reproduce when their optimal conditions are met [31]. However, in spite of having longer generation times, fungi are more efficient at decomposing organic substrates and have more stable populations [32, 33]. Although some species are phytopathogenic, there are numerous fungal species capable of antagonizing plant pathogens, stimulating vegetative growth, and decomposing plant residues [28].
The composition of soil microbial communities differs significantly both from a quantitative and qualitative point of view, being deeply influenced by the presence of nutrients, water, applied substances, and farming techniques, to name a few [34]. Most of the times, the exogenous factors play a significant role, but it has been pointed out that plant genotypes also possess the tools to intervene in the selection of root-associated microorganisms [35]. Soil is often regarded as a reservoir for the microbiota of the leaves, flowers, and grapes, as more similarities have been described between each of those aerial plant compartments and the soil than between each other [16]. Understanding the interaction between the plant and soil microbiota is essential in order to have a better grasp of the way farming practices affect the soil habitat [36].
Depending on the soil’s relation to the plant, several compartments are distinguished: the endorhizosphere, the rhizosphere, and the bulk soil [37]. The rhizosphere is the most intensely studied soil region in relation to the plant, represented by the soil located in the immediate proximity to the plant root system. Microorganisms found in bulk soil are mostly inactive in comparison with those found in the rhizosphere [36] because the latter is characterized by a high nutrient content due to the release of rhizodeposits. These secretions contain sugars, amino acids, organic acids, flavonoids, and terpenoids [11, 38], which trigger a chemotactic response for some microorganisms [39]. The composition of rhizosphere microbial communities fluctuates in accordance with the root exudate patterns specific to the plant’s vegetative cycle and health status [40, 41]. However, it has been pointed out that the high nutrient content makes the microbial diversity poorer in the rhizosphere in comparison with the bulk soil [36], as carbon inhibits the growth of some microorganisms when it is found in such quantities [6].
Like any other crop, grapevine is susceptible to diseases, which are most often controlled by using chemical pesticides. Fungal diseases pose the biggest threat, making it necessary to use fungicides constantly [16]. Due to the fact that grapevine is one of the crops that require very frequent applications of phytosanitary products, the number of pesticide treatments and the maximum allowed quantity per year has been regulated by the European Commission [42]. In conventionally treated vineyards, chemical pesticides are used, raising the incidence of problems regarding pesticide resistance and the presence of residual pesticides [43]. In organically treated vineyards, copper-based fungicides are viewed as the most important treatments for the most commonly occurring diseases, although recently copper was added to the list for substitution candidates [42].
It is a known fact that phytosanitary treatments present the unwanted potential of affecting the structure and function of the microbiota, as their spectrum is too broad to include only the target microorganisms [27]. The composition of the soil microbiota is sensitive to the action of the chemical treatments. As it was pointed out in a study comparing conventional, organic, and biodynamic cultivation systems, the greatest microbial diversity and richness were found in the soil where grapevine was grown organically [16]. As soil is one of the main reservoirs for the phyllosphere microbiota, the composition of its microbial communities has a critical role in determining the microbial communities found in relation with other plant compartments [44]. Environmental factors, pathogens, and the plant itself are elements that have a well-established role in the manifestation of diseases. The other factor that intervenes in disease development is thought to be the composition of the microbiota [45], although the mechanisms that can successfully manipulate the microbial communities in order to inhibit the occurrence of diseases is not particularly well understood [43].
An essential aim of organic viticulture is reducing the use of pesticides without affecting the yield and production of grapes. Biological fungicides based on microorganisms have been recently developed and present an advantage, as they may be applied at any given time without worrying about the residual presence of pesticides on grapes [46]. Reducing the input of synthetically obtained pesticides can also be achieved by certain farming practices, such as altering the plant microclimate in order to avoid the optimal conditions for the development of pathogens, reducing the overwintering inoculum, or applying treatments only when alerted by devices that use mathematical models and monitor environmental conditions [43].
Samples were collected from the vineyards of the Research Station for Viticulture and Enology from Murfatlar (RSVEM), Romania. The biological material consisted of grapevines of the Cabernet Sauvignon and Sauvignon blanc cultivars, grafted on
For each cultivar, a treated and an untreated plots were established. For the untreated plots, no treatments were applied in the year 2021 in order to observe the short-term effects of ceasing pesticide use on grapevine microbiota. For the conventionally treated plots, the usual treatment scheme has been applied, which involved 8 treatments during the studied year: the first treatment was applied during the dormancy period, consisting of calcium polysulfide; the second during BBCH 53, with products that have cymoxanil, mancozeb, copper oxychloride, and sulfur as active ingredients; the third during BBCH 60, with oxathiapiprolin, folpet, fenhexamid, proquinazid, and alpha cypermethrin; the fourth during BBCH 69, with oxathiopiprolin, folpet, proquinazid, fludioxonil, and cyprodinil; the fifth during BBCH 73, with fosetyl Aluminum, folpet, myclobutanil, and emamectin benzoate; the sixth during BBCH 77, with dimethomorph, mancozeb, metrafenone, boscalid, and hexythiazox; the seventh during BBCH 81, with dithianon, dimethomorph, sulfur, and emamectin benzoate; and the eighth, during BBCH 85, with copper hydroxide, sulfur, and fenhexamid.
IoT sensors were installed in the experimental plots, which were used to monitor, among others, leaf moisture. The PHYTOS 31 leaf wetness sensor measures the dielectric constant on the upper surface of the device, the value being proportional to the present water amount.
At harvest, the average production per vine for each of the 4 studied variants was calculated, in order to assess the impact of pesticide use cease on grape production.
For the study of phyllosphere microbiota, sampling was carried out in 2021 during the phenophase BBCH 79 (when most of the bunches were compacted). The samples consisted of 6 leaves taken from one grapevine per variant, on which the sensors were placed, from the base, middle, and top of the canopy. The samples were processed immediately in the microbiology laboratory of Constanta Maritime University.
Considering the fact that the phyllosphere is an oligotrophic system, in which the distribution of nutrients is heterogeneous, squares of approximately 1 cm2 were randomly chosen for each part of the leaf, which were cut with a sterile scalpel. In order to observe the cut sections under the epifluorescence microscope (N-400FL type with blue filter), they were subjected to an adhesive tape gluing process. The adhesive tape was stained with specific fluorochromes and then placed on a microscopic slide. By applying this technique, it is possible to recover the cuticle from leaves, trapping the microorganisms between the tape and the cuticle. Thus, a very high recovery of cells is permitted, while preserving spatial information.
Using this method, a total number of cells/analyzed surface is obtained, at the same time observing the physiological state of the microorganisms, using specific fluorochromes: SYBR Green (SYBR Green I nucleic acid gel stain. 10,000× in DMSO, Sigma Aldrich 5 ml) and Propidium Iodide (≥94% HPLC, Sigma Aldrich 10 mg). The staining solution is prepared in a ratio of 1:1, and applied for 8−10 minutes, according to [47, 48, 49].
The efficiency of fluorescent compounds for evaluating the integrity of cell membranes is determined by selectivity, brightness, excitation, and maximum emission. The final SYBR Green concentration is 10 μl/ml and 10 μg/ml for Propidium Iodide. For each sample, 20 microscope fields were quantified. To visualize the bacteria and fungi on the phyllosphere, the blue filter with a wavelength of 450−480 nm was used, specifically for the chosen fluorochromes. Images were taken with a digital camera and further used for automatic processing, using the “CellC” cell counting software, according to [50].
For the identification of soil fungi, sampling was carried out in three stages during the year 2021, according to the BBCH phenophases of the grapevine: the first stage was BBCH phenophase 11 (appearance of the first leaf), the second was BBCH phenophase 79 (when several bunches were compacted), and the third was BBCH phenophase 97 (end of leaf fall). From each plot, the soil was collected from the horizon 0−10 cm, from the base of the grapevine trunks, analyzing a total of 12 samples.
Soil samples were processed in the RSVEM microbiology laboratory. The applied technique involves the cultivation of fungi on solid culture media, using the method of serial decimal dilutions. A volume of 0.1 ml of each dilution was spread on the surface of the Rose Bengal CAF Agar (RBCA) medium in triplicate. The plates were incubated at 25° Celsius, being checked initially after 72 h, then daily to observe the growth of the colonies. To avoid redundant isolation of the strains, for each morphotype with specific traits, the colonies present on the 3 plates were counted for the optimal dilution, after which they were isolated on potato dextrose agar (PDA). The modified slide culture method [51] was applied in order to allow a more efficient observation of the fungal structures under the microscope. The fungal strains were identified based on morphological criteria to the genus level, according to [52, 53]. Although this method offers information on the main fungal taxa present in soil, it is important to mention that a more detailed research would have been possible with the aid of molecular identification techniques.
For the identified soil fungi, the frequency for each genus was calculated according to the formula Di = (Ni/N) ×100, where Di = the frequency of genus i; Ni = UFC number for gender i; and N = total number of CFUs. According to this formula, the genus frequency can be grouped into several classes: <0.5% = rare, ≥0.5 < 1.5% = occasional, ≥1.5 < 3.0% = common, and ≥3.0% = abundant [54]. The ANOVA test was applied to determine whether there were statistically significant differences between the number of CFUs for the sampling phenophases, and the t test was used for treatment types and grapevine cultivars, taking into account a significance level of 5%. For the fungal populations, Sørensen’s similarity index and Shannon diversity index were calculated.
Microscopy analyses revealed that bacteria are prevalent in epidermal cell grooves, around trichomes and the stomatal opening, and less prevalent on the elevated surface of epidermal cells. Bacteria are the most abundant microbial group in the phyllosphere, followed by fungi. The measured density of bacteria is from 103 to 107 cells per square centimeter of leaf tissue (Figure 1), while the density of fungal structures is ranging from 102 to 104 cells per square centimeter of leaf tissue (Figure 2).
Average number of bacterial cells ×105 on leaves from the base (a), middle (b), and top (c) of the canopy, from the adaxial (AD) and abaxial (AB) sides, for sauvignon blanc (SB) and cabernet sauvignon (CS).
Average number of fungal structures ×104 on leaves from the base (a); ×103 on leaves from the middle (b), and top (c) of the canopy, from the adaxial (AD) and abaxial (AB) sides, for sauvignon blanc (SB) and cabernet sauvignon (CS).
As it can be seen, the untreated plots show a significantly higher number of microorganisms per square centimeter, at least an order of magnitude greater in comparison with the treated ones, for both cultivars and for all canopy compartments. Microorganisms are placed in higher density on the abaxial side of the leaf, respectively, on the leaves from the base of the canopy. The difference in microbial density between the two vine varieties can also be attributed to the mesoclimate (hill vs. valley), as the plots where Cabernet Sauvignon is cultivated are located at a higher altitude and micro currents can form, that can reduce the humidity conditions favorable to the microbiota development.
The presence of the analyzed microbiota on the abaxial surface of the leaf is probably due to stomata, which represent a natural entry pathway for endophytic microorganisms. The laminar layer may also play a significant role, as moisture emitted by stomata can be retained at this level, reducing the water stress of epiphytic microorganisms. Leaf wetness was analyzed in both plots using IoT sensors (Figure 3). In the studied period, the highest leaf wetness values were observed for the leaves from the untreated Sauvignon blanc plots, which also harbored the greatest number of microorganisms.
Leaf wetness in the studied plots.
Epiphytic microbiota has a first contact with the leaf cuticle, which may contain a higher or lower amount of wax that may prevent bacterial colonization [55]. Bacterial aggregates can lead to the formation of biofilms on the leaf surface, which represent a form of adaptation that offers protection against desiccation. Phyllosphere-colonizing bacteria can alter the environment in order to modify the plant’s immune system, reflected in differential host responses. Clearly, these bacteria are very dense (107/cm2) and contribute to many processes in the behavior of the individual plants. The results of the analysis done directly on the leaf surface show that biofilms may be tens of micrometers thick and could form extensive networks that cannot be quantified (Figure 4). Biofilms contain multiple microbial species and could create physical barriers on the leaf surface and establish chemical gradients, promoting metabolic exchange. The biofilm could protect the microbial community under adverse conditions and confer them a survival and colonization selective advantage. Extracellular polymeric substances are usually produced, having the role of maintaining the foliar surface hydrated and concentrating detoxifying enzymes at the same time [24].
Fluorescence micrograph of the microorganisms colonizing a grapevine leaf. Yellow arrow − Bacteria present on plant veins; black arrow − fungal hypha; and white arrow − fungal structures.
Even though the results illustrate the fact that pesticide use influences phylloplane microbiota in a negative way, it is important to mention the impact of ceasing pesticide use on grape production, as the untreated grapevines were affected by diseases; for the Sauvignon blanc cultivar, there was a 50.6% decrease in grape production for the untreated variant when compared with the treated one, while for the Cabernet Sauvignon cultivar, the untreated variant had on average 33.6% lower production in comparison with the treated variant. Thus, the variant that harbored the highest number of microorganisms per square centimeter also showed the lowest grape production.
A total of 123 strains were isolated, 44 for the BBCH 11 phenophase, 29 for the BBCH 79 phenophase, and 50 for the BBCH 97 phenophase. In terms of frequency (Figure 5), out of the 12 genera identified, the following were classified as abundant:
Frequency of the isolated fungal strains. AB – Abundant; OC – Occasional; R – rare.
None of the isolated fungal strains presented sexual structures, only the anamorphic stage being observed. As a taxonomic classification, all genera belong to the phylum Ascomycota, except the genus
A very recent study pointed out that most of the isolated genera, such as
From a statistical point of view, the differences between the treated and the untreated experimental plots in terms of the diversity of isolated genera are not significant (P = 0.55, F < F crit).
No statistically significant differences were reported with respect to the studied phenophases or the grapevine cultivars compared to the types of identified fungi. The calculated Shannon index had a higher value for the untreated plots (2.253), in comparison with the treated plots (2.139), whereas the calculated value for Sørensen’s similarity index was 73.68%.
Fungal communities found in agricultural soils are influenced by factors, such as soil type, available nutrients, edaphic properties, plant communities, and agrotechnical practices, as well as climatic conditions [58]. The importance of the latter has been highlighted in a study that showed that climatic factors were probably the leading element that caused a variation in fungal communities from 1 year to another [59]. Water stress is a factor known to impact the composition of soil fungal communities [60].
A great number of soil micromycetes are active where readily assimilable elements are found, thus making the soil a “world of asexual microfungi” [61]. Fungi are generally involved in the decomposition of organic matter, the cycling of nutrients, soil aggregates formation, and the mobilization of minerals, among others [62]. Moreover, fungi are extremely adaptable, as they are able to react to detrimental conditions by modifying their form [63].
Concerning phylloplane microbiota, the differences between the treated and untreated plots were obvious, with the untreated leaves showing considerably greater numbers of microorganisms for both of the studied cultivars. Thus, the effects of ceasing pesticide use can be readily seen on ephemeral plant structures, such as the leaves.
However, when comparing soil fungi from a quantitative point of view, no significant differences can be seen after only 1 year between the treated and the untreated plots, statistically speaking. This can be due to the fact that pesticides can still persist in the soil residually, affecting microbial populations.
This work was funded by the Romanian National Authority for Scientific Research and Innovation, CCCDI - UEFISCDI, for the COFUND-ICT-AGRI-FOOD-MERIAVINO-1, project number 203, within PNCDI III.
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Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. 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Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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She has more than fifteen years of teaching and research experience. She has published more than 550 scientific publications/communications, including 15 books, 50 book chapters, 100 original research papers, 380 research communications in national and international conferences, and 12 patents. She is a member of the editorial board of five journals and acts as a reviewer for several national and international journals. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. 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She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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Dr. Şentürk currently works as an professor of Biochemistry in the Department of Basic Pharmacy Sciences, Faculty of Pharmacy, Ağri Ibrahim Cecen University, Turkey. \nDr. Şentürk published over 120 scientific papers, reviews, and book chapters and presented several conferences to scientists. \nHis research interests span enzyme inhibitor or activator, protein expression, purification and characterization, drug design and synthesis, toxicology, and pharmacology. \nHis research work has focused on neurodegenerative diseases and cancer treatment. 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He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,series:{id:"11",title:"Biochemistry"}}},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/41706",hash:"",query:{},params:{id:"41706"},fullPath:"/profiles/41706",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)}()