\r\n\t(i) Quantum dots of very high-quality optical applications, Quantum dot light-emitting diodes (QD-LED) and ‘QD-White LED’, Quantum dot photodetectors (QDPs), Quantum dot solar cells (Photovoltaics).
\r\n
\r\n\t(ii) Quantum Computing (quantum bits or ‘qubits’), (vii) The Future of Quantum Dots (broad range of real-time applications, magnetic quantum dots & graphene quantum dots), Superconducting Loop, Quantum Entanglement, Quantum Fingerprints.
\r\n
\r\n\t(iii) Biomedical and Environmental Applications (to study intracellular processes, tumor targeting, in vivo observation of cell trafficking, diagnostics and cellular imaging at high resolutions), Bioconjugation, Cell Imaging, Photoelectrochemical Immunosensor, Membranes and Bacterial Cells, Resonance Energy-Transfer Processes, Evaluation of Drinking Water Quality, Water and Wastewater Treatment, Pollutant Control.
",isbn:"978-1-80356-594-1",printIsbn:"978-1-80356-593-4",pdfIsbn:"978-1-80356-595-8",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"0dd5611c62c91569bd2819e68852002a",bookSignature:"Prof. Jagannathan Thirumalai",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11756.jpg",keywords:"LED, Organic LEDs, Dyes & Pigments, Solar Cells, Laser Photonics, Electronic Switching Devices, Qubits, Josephson Junction, Bioconjugation, Cell Imaging, Photoelectrochemical Immunosensor, Membranes, and Bacterial Cells",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 16th 2022",dateEndSecondStepPublish:"May 27th 2022",dateEndThirdStepPublish:"July 26th 2022",dateEndFourthStepPublish:"October 14th 2022",dateEndFifthStepPublish:"December 13th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. J. Thirumalai received his Ph.D. from Alagappa University, Karaikudi, He was also awarded the Post-doctoral Fellowship from Pohang University of Science and Technology (POSTECH), the Republic of Korea. His research interests focus on luminescence, self-assembled nanomaterials, and thin-film optoelectronic devices. He has published more than 60 SCOPUS/ISI indexed papers and 11 book chapters, edited 4 books, and member of several national and international societies like RSC, OSA, etc. His h-index is 19.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"99242",title:"Prof.",name:"Jagannathan",middleName:null,surname:"Thirumalai",slug:"jagannathan-thirumalai",fullName:"Jagannathan Thirumalai",profilePictureURL:"https://mts.intechopen.com/storage/users/99242/images/system/99242.png",biography:"Dr. J. Thirumalai received his Ph.D. from Alagappa University, Karaikudi in 2010. He was also awarded the Post-doctoral Fellowship from Pohang University of Science and Technology (POSTECH), Republic of Korea, in 2013. He worked as Assistant Professor of Physics, B.S. Abdur Rahman University, Chennai, India (2011 to 2016). Currently, he is working as Senior Assistant Professor of Physics, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam (T.N.), India. His research interests focus on luminescence, self-assembled nanomaterials, and thin film opto-electronic devices. He has published more than 60 SCOPUS/ISI indexed papers and 11 book chapters, edited 4 books and member in several national and international societies like RSC, OSA, etc. Currently, he served as a principal investigator for a funded project towards the application of luminescence based thin film opto-electronic devices, funded by the Science and Engineering Research Board (SERB), India. As an expert in opto-electronics and nanotechnology area, he has been invited as external and internal examiners to MSc and PhD theses, invited to give talk in some forum, review papers for international and national journals.",institutionString:"SASTRA University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"10",totalChapterViews:"0",totalEditedBooks:"6",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"17",title:"Nanotechnology and Nanomaterials",slug:"nanotechnology-and-nanomaterials"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"347258",firstName:"Marica",lastName:"Novakovic",middleName:null,title:"Ms.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"marica@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"5348",title:"Luminescence",subtitle:"An 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1. Introduction
Cyanobacteria are the only up-to-date known prokaryotes capable of oxygenic photosynthesis. They assimilate light energy via electron transfer from water to oxidized ferredoxin, and excrete triplet dioxygen as a waste product. The majority of cyanobacteria possess chlorophyll a (Chl a), bicyclic carotenoids, and phycobiliproteins (PBP). In common, these pigments absorb light quanta within a whole visible area (400–700 nm). At the same time, some cyanobacteria have been shown to use far-red light (FRL) with wavelengths of more than 700 nm [1]. For this purpose, red-shifted Chl d and/or Chl f are produced [2, 3]. In search of novel representatives of FRL-adapting cyanobacteria, various analytical methods are employed, especially those based on fluorescence detection.
This chapter includes four sections, which describe: photosynthetic apparatus in cyanobacteria; photoacclimation phenomena in cyanobacteria; fluorescence methods employed in the study of FRL-adapting cyanobacteria; FRL photoacclimating strains in CALU collection (St. Petersburg University, St. Petersburg, Russia).
2. Photosynthetic apparatus in cyanobacteria
Light energy assimilation machinery in cyanobacteria is usually localized in/on intracytoplasmic membrane structures termed thylakoids. Among principal constituents are: reaction centers (RC) of two photosystems (PSs), a main light-harvesting complex (LHC), and electron transfer chain (ETC). The essential component of PSII is unique light-dependent enzyme—H2O dehydrogenase, or water oxidizing complex (WOC).
In the majority of cyanobacteria, Chl a represents the only chlorophyll while in rare cases other chlorophylls are additionally produced [4, 5].
Both PSs are supplied with their respective light-harvesting antennae, which contain Chl a. LHC is usually represented by phycobilisome (PBS), supramolecular aggregate of phycobiliproteins (PBP) stabilized by colorless linker polypeptides, and anchored to thylakoid surface with high-molecular mass colored polypeptide [6, 7]. PBP apoprotein moiety is represented by α or β subunits that covalently bind non-porphyrin tetrapyrrole chromophores [8, 9]. Depending on chromophore type and number, four main PBP types are distinguished: phycocyanin (PC), phycoerythrin (PE), allophycocyanin (APC), and phycoerythrocyanin (PEC) [8]. Standard PBS consists of the central core and peripheral rods. The former contains two to three “cylinders” assembled of several (αβ)3 trimers each, while the latter includes several [2(αβ)3] hexamers each. Due to anisotropic structure of rods and core, a waste-less channeling of excitation energy to RC chlorophyll is achieved [10, 11].
3. Photoacclimation phenomena in cyanobacteria
Cyanobacteria can acclimate themselves to light quantity and quality, that is, they adaptively respond to the shifts in ambient light color and intensity. For this purpose, they can modulate: (1) PS content, (2) the interaction between PS and PBS, and (3) LHC structure, PBP content in particular.
In the first case, antenna size varies inversely with a flood of light quanta. This strategy is performed via the changes in thylakoid surface and PS packing density [12].
In the second case (given that ambient light is enriched with either short- or long-wavelength quanta), excitation energy equally distributes between long-wavelength PSI and short-wavelength PSII. This phenomenon is termed State 1 ↔ State 2 transition. State 1 is achieved in response to relative over-excitation of PSII [13]. Here, PBS behaves as a mobile antenna, and it laterally moves from PSII to PSI. In the opposite situation (State 2), PBS detaches from PSI and comes back to PSII [14]. State transition scenario is as follows: up- or downshifts in ETC reduction level → redox-sensitive (de)phosphorylation of proteins within the PBS baseplate → coulomb attraction/repulsion between PBS and PS [15].
In the third case, PBS absorbance peak adjusts to ambient light color (preferentially green or red). This adaptation is performed via the shifts in PE (green light absorbing PBP) and PC (red light absorbing PBP) content. The knowledge on this phenomenon termed complementary chromatic adaptation (CA) has been comprehensively reviewed [16, 17].
In agreement with a response to green or red light, cyanobacteria have been ascribed to three CA groups [18]: group СА1 (steady PE and PC content), group СА2 (PE content varies), and group СА3 (PE and PC contents vary).
Groups CA2 and CA3 are specified according to the presence of regulatory photoreceptors CcaS and RcaE, which belong to the “cyanobacteriochromes” phytochrome family [19, 20, 21]. These receptors contain one and the same bilin-binding domain GAF (cyclic guanosine monophosphate phosphodiesterase/adenylyl cyclase/FhlA), which regulates green or red light-triggered photocycle [22, 23]. In the case of CA2, under green light, CcaS (signal transducer) phosphorylates transcription factor CcaR (response regulator) that induces the production of PE [22]. In the case of CA3, under red light, RcaE phosphorylates transcription factors RcaF and RcaC, which regulate numerous participants of PC and PE biosynthesis pathway [23, 24].
Group CA4 is represented by marine Synechococcus strains grown under blue or green light [25, 26, 27]. Under blue light, PE phycoerythrobilin chromophores replace phycourobilin chromophores, which adapt PBS to the use of smaller wavelength light. Although corresponding photoreceptor is unknown, light signal was shown to be transmitted to transcription factors FciA and FciB with a participation of chromophore liases MpeZ and MpeW [26, 28].
Group CA5 is typical of Acaryochloris marina [29]. Irrespective of light conditions, this cyanobacterium uses membrane-embedded LHC containing Chl d. Under red light (625 nm), atypical rod-like PC and APC containing PBS is additionally produced [30]. Such PBS disassembled in far-red light (720 nm), and corresponding PBP are destroyed [31, 32]. In other words, in these light conditions, A. marina uses two LHCs in common. Although the underlying regulatory mechanism is unknown, A. marina genome has been shown to contain a motif similar to that coding for FRL accepting phytochromes [33].
Group CA6 is represented by several strains of cyanobacteria, which adaptively produce Chl f and/or Chl d in FRL [16]. In more detail, this phenomenon is dwelled on below.
Group CA7 [16] has been described in the case of cyanobacteria which synthesize yellow-green light absorbing PEC, and regulate the amount of this PBP light dependently. Similar to СА2, the adaptive response is under control of two-component regulatory system CcaS/CcaR.
Another type of reaction to green or red light is typical for СА0 group [16]. In this case, PBP content is stable, while the amount of CpcG2 (CpcL) and CpcG1 linker polypeptides varies reciprocally [34]. In green light, the CcaS/CcaR system upgrades the production of CрcG2 (CpcL) linker involved in the biogenesis of APC lacking PBS [22]. The CрcG2 (CpcL) type PBS is suggested to be rod-like, and it possibly supplies energy to PSI under suboptimal conditions [16, 22].
Cyanobacterial response to FRL has been described in cyanobacteria only recently, and it is based on red-shifted Chl d and/or Chl f (697 and 706 nm maxima in methanol, respectively). First representative of Chl d-containing species was A. marina [1], while the initially described Chl f-containing species was Halomicronema hongdechloris [35].
Cyanobacteria can use two main strategies of FRL photoacclimation.
In the first strategy, Chl d represents the bulk chlorophyll, and it is constitutively produced in A. marina [36, 37, 38, 39]. The pigment participates in PSI and PSII, as well as in LHC [40, 41, 42, 43]. Such strategy is observed in cyanobacteria inhabiting marine or continental haline water bodies poor in visible light but rich in FRL.
In the second strategy, Chl a represents the bulk chlorophyll while Chl f (sometimes together with a small amount of Chl d) is produced adaptively in FRL [35, 44, 45, 46]. In this case, ~10% of Chl a in PSI and PSII is replaced with Chl f; special paralogue polypeptides substitute for PSI and PSII subunits; PBS is remodeled [45]. The latter strategy is termed FRL photoacclimation—FaRLiP [3], or СА6 (see above). In this strategy, FRL induces the expression of 21 genes of FaRLiP cluster including those coding for PSI and PSII subunit paralogues [2, 45, 46]. The products of these genes specifically bind Chl a together with red-shifted chlorophylls [2]. FaRLiP cluster also includes the genes coding for PBP subunit paralogues [45, 47]. Resulting PBS are optimal in new light climate because they are devoid of short-wavelength PBP (PE and PEC), and correspondingly lack rod periphery [46].
Leptolyngbya sp. JSC-1 grown in white light (WL) or red light (RL) contains the standard five-cylinder-core PBS, while in FRL a two-cylinder-core PBS is produced [45]. The latter has a 708-nm maximum (40 nm longer than in APCB, the top wavelength PBP previously reported). In its turn, FRL-grown H. hongdechloris produces a two-cylinder-core mini PBS, which contains APC with 653- and 712-nm maxima [48].
FRL-grown Synechococcus sp. PCC 7335 produces PBS of two types: three-cylinder-core PBS containing PC and APC, and two-cylinder-core PBS, which contains only APC with FaRLiP gene encoded subunits, and displays red-shifted 650- and 711-nm light absorbance and 730-nm fluorescence emission maxima [49].
FaRLiP response falls under the control of two-component phosphorelay system [47]. Sensory component (RfpA photoreceptor) represents a far-red light regulated cyanobacteriochrome with histidine kinase domain. Response regulators RfpB and RfpC have two CheY-like signal accepting domains, which flank the DNA-binding domain. RfpB acts as a transcription activator for FaRLiP genes [47, 50]. Within this phoshorelay, RfpA histidine kinase becomes (de)activated, and that influences the RfpB key response regulator. In its turn, RfpC is involved in transfer of phosphoryl group from RfpA to RfpB.
4. Fluorescence methods employed in the research of far-red light-adapting cyanobacteria
4.1 Steady-state fluorescence detection
Absorbed energy of light quanta brings photosynthetic pigments into excited state, which is relaxed by: (1) productive energy assimilation in the form of charge separation within RC, (2) counterproductive energy dissipation into heat, or with fluorescence quanta [51]. Since the photosynthetic apparatus is less than 100% effective, the second mechanism is universally in action, although it depends on environmental and physiological regimes.
Steady-state fluorescence detection helps to identify photosynthetic pigments because they demonstrate individual fluorescence excitation and emission spectra. Additionally, this method can detect energy transfer between pigments. Fluorescence spectra can be obtained at either room or cryogenic temperature (most frequently, 77 K). Low temperatures are preferred because of lowered molecular mobility, and due to smaller intramolecular vibrations; as a result, peaks become higher and better resolved [51]. Importantly, low-temperature regime (4–77 K) helps to discriminate PSI chlorophyll (~ 720 nm) and PSII chlorophyll (~ 685 nm) emission peaks [51, 52, 53].
Current data on steady-state fluorescence of red-shifted chlorophylls are few. For instance, the spectra of WL-grown H. hongdechloris were compared with those of FRL culture [35]. In the case of WL cells, Chl a-specific 405-nm fluorescence excitation yielded fluorescence emission maxima at 640, 658, 682, and 730 nm of comparable height. In contrast, FRL cells exhibited a major 748-nm maximum (indicating the adaptive synthesis of red-shifted Chl f), and 682- and 720-nm minor maxima, respectively. Also, the emission at 440-nm excitation was compared in Calothrix sp. PCC 7507, Chlorogloeopsis sp. PCC 9212, Chroococcidiopsis sp. PCC 7203, Fischerella sp. PCC 7521, and Synechococcus sp. PCC 7335 grown under different light conditions [46]. In all these strains, WL cultures showed emission maxima of Chl a (683–684 nm, 693–695 nm, and 718–727 nm). At the same time, FRL spectra were strain specific, Chl f being most pronounced (Calothrix sp. PCC 7507—736 nm; Chlorogloeopsis sp. PCC 9212—739 nm; Chroococcidiopsis sp. PCC 7203—718, 736, and 750 nm; Fischerella sp. PCC 7521—749 nm; Synechococcus sp. PCC 7335—738 nm).
Apart from the experiments on cell suspensions, steady-state fluorescence of Chl a and Chl f has been detected with subcellular fractions, as well as with PSI, PSII, and PBS preparations. For example, H. hongdechloris emission was compared for WL and FRL thylakoid preparations [54]. In WL preparation, major 710-nm peak as well as 680- and 732-nm minor peaks were observed, while FRL preparation demonstrated a major 740-nm peak due to adaptively produced Chl f.
Cryogenic detection helped to monitor energy transfer in FRL-adapted Synechococcus sp. PCC 7335 [55]. Purified PSI or PSII preparations obtained from FRL-adapted cells showed one and the same well-expressed peak at 738–740 nm indicating similar effectiveness of energy coupling notwithstanding a distinction in PS structure.
Steady-state fluorescence emission was also detected in the experiments with PBP-specific 590-nm excitation of RL- or FRL-grown Synechococcus sp. PCC 7335. Besides 643-, 658-, and 679-nm peaks in the RL culture, FRL-grown cells acquired 717- and 737-nm maxima that could be explained by PBS rearrangement due to induction of FaRLiP gene cluster [50].
4.2 Time-resolved fluorescence spectroscopy
The method helps to analyze molecular processes within a picosecond/nanosecond timescale [56]. Because primary photosynthetic processes take several femtoseconds/nanoseconds, time-resolved fluorescence spectroscopy can trace corresponding rates and pathways of energy transfer [57]. Recently, this method has helped to elucidate the role(s) of red-shifted chlorophylls in cyanobacterial PS.
In the case of Chl f-producing H. hongdechloris, time-resolved fluorescence detection was performed at 77 K, with 425-nm excitation [41]. In WL cells, the excitation raised 685-nm (F685) and 730-nm (F730) emission peaks characteristic for PSII and PSI correspondingly. A shift from F685 to F695 occurred with time, and an extra F742 peak appeared and decayed. In FRL cells, F685 and F748 maxima were observed. The former quenched rapidly indicating energy transfer from PSII to PSI. F748 signified the presence of Chl f, and uphill energy transfer (from Chl f to Chl a) was proposed.
Time-resolved fluorescence detection within a picosecond/femtosecond time-scale was also performed with H. hongdechloris thylakoid membrane preparations [54]. Similar to WL cells, WL membrane emission belonged to PSI and PSII Chl a (727 and 685 nm correspondingly). However, these peaks were attended with PBP-specific 678-nm peak. In the case of FRL membrane, main fluorescence emission maxima belonged to Chl a (685 nm) and Chl f (745 nm). In FRL membrane preparations, fluorescence rise and decay curves suggested Chl f to be energy donor to Chl a.
Later, the reality of uphill energy transfer from Chl f H. hongdechloris was confirmed in the experiments using TCSPC (time-correlated single photon counting) and DAS (decay-associated spectra) methods [58]. According to obtained data, energy was transferred within PSII on the route: PBS → Chl f → Chl a; only then charge separation began, and Chl a represented the P680 primary donor. Thus, Chl f in H. hongdechloris is PSII antenna pigment, and it does not participate in charge separation.
At the same time, the involvement of red-shifted chlorophylls in charge separation within PSI and PSII was proposed for Chroococcidiopsis sp. PCC 7203, which adaptively produces Chl d and Chl f [59, 60]. Data obtained using plenty of methods, TCSPC in particular, showed that RCI in the FRL-adapted cells contained 7–8 Chl f molecules, together with 90 Chl a molecules. Special pair (PA and PB) was represented by Chl a and Chl a epimer (a′). Primary acceptors (A0A and A0B) were also Chl a molecules. Chl f molecule absorbing 745-nm light was associated with primary donor (A−1A and/or A−1B). In its turn, RCII contained 4 Chl f molecules, 1 Chl d molecule, and 30 Chl a molecules. Primary donors (ChlD1) were either Chl d or Chl f molecules absorbing 727-nm light.
Room temperature and 77 K fluorescence data in unicellular strain KC1 producing Chl f in FRL helped to obtain TRS (time-resolved) and DAS (decay-associated) emission spectra [61]. In the experiments with PSI exited with 405-nm laser, energy was rapidly transferred to Chl f (emission maxima 720–760 and 805 nm). In the case of 630-nm excitation (light absorbed by PBP associated with PSII), energy was transferred to Chl f (emission maxima 720–770 and 810 nm) and Chl a (emission maximum 694 nm). Distinctions in Chl f fluorescence show that PSI and PSII contained Chl f molecules with different properties, and possibly they represented antenna pigments.
Similarly, femtosecond pump-probe spectroscopy of Fischerella thermalis PCC 7521 [62] showed that Chl f represented PSI antenna pigment; it was also argued that energy was transferred uphill from Chl f to P700 Chl a special pair.
Time-resolved fluorescence spectroscopy also helped to analyze energy transfer in RL-or FRL-grown cells of Synechococcus sp. PCC 7335 [55], as well as in A. marina mini PBS [63].
4.3 Confocal laser scanning microscopy (CLSM)
The method is based on the auto fluorescence of photosynthetic pigments, chlorophylls in the first instance [64]. CLSM permits to distinguish individual pigments based on their emission peaks, to evaluate peak intensity, and to localize pigments in situ. Highly sensitive up-to-date CLSM microscopes help to obtain 3D images of cells and tissues, and to analyze dynamic physiological processes. Unfortunately, the potentiality of this method, at least with regard to FRL-adapting cyanobacteria, is underestimated, and thus corresponding data are few.
CLSM has been applied to the study of Chl d and PBP localization in Candidatus Acaryochloris bahamensis, an epibiont of colonial ascidian Lissoclinum fragile [65]. In 3D reconstruction, these pigments were asymmetrically distributed throughout cell interior and that possibly promoted optimal sunlight absorption within L. fragile tunic. Acaryochloris-like cells were also shown to inhabit the surface of Cystodytes dellechiajei ascidian body, and CLSM data indicated the presence of Chl d and PBP in this cyanobacterium [66].
Anisotropic distribution of pigments was also observed in H. hongdechloris, which adaptively produces accessory Chl f in FRL [67]. In WL culture, chlorophylls and PBP were shown to co-localize at cell periphery. However, in FRL culture, PBP fluorescence was detected only at cell poles. In their turn, FRL-grown Synechococcus sp. PCC 7335 demonstrated lower PBP fluorescence and higher chlorophyll fluorescence compared with WL cells. In FRL culture, PBP fluorescence was spatially isolated from chlorophyll fluorescence: the latter occupied cell center indicating that PBS detached from PS.
5. Far-red light photoacclimating strains in the CALU collection
5.1 Growth conditions
An ability of FRL-dependent synthesis of Chl f in trichome-forming strain Chlorogloeopsis sp. CALU 759 and unicellular strain Synechocystis sp. CALU 1173 has been deduced from whole-cell light absorbance spectra (Figure 1) as well as according to methanol extract spectrometry and HPLC results.
Figure 1.
Light micrographs and light absorbance spectra of Chlorogloeopsis sp. CALU 759 (a, c) and Synechocystis sp. CALU 1173 (b, d) cells. Dashed line, white light-grown cells; solid line, far-red light-grown cells. Arrow, shoulder explained by absorbance of red-shifted chlorophyll. Scale bar, 10 μm (a) or 5 μm (b).
Liquid cultures were grown for a 2- to 3-week time in modified BG-11 medium [68], at 20–22°С, under permanent WL (500 lx) or FRL illumination (LED with ~750 nm emission maximum).
5.2 Steady-state fluorescence detection protocol
Cell pellet was washed with 100 mM HEPES buffer (pH 8.0), and resuspended in 50 mM HEPES (pH 8.0) with or without the addition of 25% glycerol. Room temperature fluorescence was detected in a Cary Eclipse scanning spectrophotometer/fluorimeter (Agilent Technologies, Santa Clara, CA, USA), and obtained data were treated with a Cary Eclipse Scan program. Emission spectra were detected for excitation at 440 nm (specific for chlorophyll) and 550 nm (specific for PBP). Excitation spectra (chlorophyll fluorescence at 745 nm) were also detected.
5.3 CLSM protocol
The method helped to reveal photosynthetic pigments topography. Life preparations of WL- or FRL-grown cells were observed in a Leica TCS SP5 MP STED confocal microscope (Leica Microsystems GmbH, Germany). Fluorescence was raised with a 458-nm-wavelength nitrogen laser. The images were obtained in XYZ scanning regime with a 0.5-μm step. The following emission channels were used: 560–600 nm (PE), 620–650 nm (PC), 670–700 nm (Chl a), and 705/710–790 nm (Chl f). The obtained images were analyzed with a LAS AF program.
5.4 Steady-state fluorescence detection of chlorophylls and phycobiliproteins in white light- or far-red light-grown cells
Fluorescence emission spectra (Figures 2(a,b) and 3(a,b)) obtained at chlorophyll-specific 440-nm excitation demonstrated 670–740 nm maxima. In WL-grown Chlorogloeopsis sp. CALU 759 (Figure 2(a)), it was a single peak with a shoulder while in Synechocystis sp. CALU 1173 (Figure 3(a)) twin peaks were observed. In FRL-grown cells of both strains, a single 725–740 nm emission peak was observed (Figures 2(b) and 3(b)). In agreement with the previous data obtained with FRL-grown cyanobacteria [35, 46], peaks with more than 720-nm wavelength (observed in light absorbance or fluorescence emission spectra) corresponded to Chl f.
Figure 2.
Fluorescence spectra of white light (WL)-grown (a, c) or far-red light (FRL)-grown (b, d) Chlorogloeopsis sp. CALU 759 cells. Emission spectra: 440 nm (a, b) and 550 nm (c, d) excitation. Excitation spectra: chlorophyll fluorescence at 745 nm (e, f).
Figure 3.
Fluorescence spectra of white light (WL)-grown (a, c) or far-red light (FRL)-grown (b, d) Synechocystis sp. CALU 1173 cells. Emission spectra: 440 nm (a, b) and 550 nm (c, d) excitation. Excitation spectra: Chlorophyll fluorescence at 745 nm (e, f).
Fluorescence emission spectra obtained with PBP-specific 590-nm excitation resulted in 645–660 nm emission peak with broad 690–715 nm shoulder. In WL-grown Chlorogloeopsis sp. CALU 759 (Figure 2(c)), the shoulder was steep while in Synechocystis sp. CALU 1173 (Figure 3(c)) it was gently sloping.
In FR-grown cells, larger size 645–660 nm peak was attended with smaller 715–725 nm peak (Figures 2(d) and 3(d)). Modified emission spectra, as compared with WL-grown cells, could be explained by some changes in PBS arrangement and behavior during the FaRLiP response [45, 48, 49].
The comparison of emission spectra in Figures 2 and 3 showed that, in the case of FRL-grown cells, direct excitation of chlorophylls (blue light excitation, 440-nm wavelength) was more efficient than via antenna PBP pigments (550-nm green light exciting mainly PE and PC). This effect can be explained by PBS uncoupling with PSII because bulk fluorescence at room temperature is known to issue from PSII. If not coupled, PBP excited at 550 nm should exhibit considerable fluorescence around 700 nm (that is the case here).
Fluorescence excitation spectra of WL-grown cells (Figures 2(e) and 3(e)) demonstrated a single peak at 625–650 nm excitation wavelength. Negligible fluorescence during illumination with 400–500 nm light (which preferentially excited chlorophylls) can be explained by shading of the blue region with photosynthetically inactive carotenoids. At the same time, fluorescence during illumination with ~600 nm (which preferentially excited PBP) raised a maximum chlorophyll fluorescence that additionally indicated an efficient coupling of PBS with PSII. In contrast to WL-grown cells, in FRL-grown cells (Figures 2(f) and 3(f)), peak chlorophyll fluorescence was observed at ~670 and ~ 720 nm excitation. High ratio of 720-nm peak/670-nm peak size additionally witnesses for a change of photosynthetic apparatus at FaRLiP response. It is noteworthy that unlike cryogenic method, room temperature fluorescence detection could not comment on the specificity of Chl f functioning in analyzed strains (that is the aim of our future research).
5.5 CLSM of chlorophylls and phycobiliproteins in white light- and far-red light-grown cells
In the case of WL-grown Chlorogloeopsis sp. CALU 759 (Figure 4), false-green 670–700 nm fluorescence typical for Chl a (Figure 4(a)) and false-blue 620–650 nm fluorescence typical for PC (Figure 4(b)) were observed not only at cell periphery but also in cell center. Moreover, mutually overlapping fluorescence channels (Figure 4(c)) indicated that the pigments had a common localization.
Figure 4.
Laser confocal scanning micrographs of white light-grown Chlorogloeopsis sp. CALU 759 cells. Chlorophyll a fluorescence is shown with false-green (a); phycocyanin fluorescence is shown with false-blue (b); chlorophyll a and phycocyanin total fluorescence (c).
Similar topology was observed in FRL-grown Chlorogloeopsis sp. CALU 759 (Figure 5). Importantly, Chl a false-green emission (Figure 5(a)), Chl ffalse-purple emission (Figure 5(b)), and Chl a/Chl f mixed emission (Figure 5(d)) coincided spatially, and that indicated a relative integrity of photosynthetic apparatus. At the same time, PC fluorescence showed a more homogeneous distribution (Figure 5(c,e,f)). Thus, PBS either partially detached from thylakoid membrane, or PBS coupling with PSII lowered (which is in agreement with steady-state fluorescence results). Similar images were obtained for FRL-grown Synechococcus sp. PCC 7335 [67].
Figure 5.
Laser confocal scanning micrographs of far-red light-grown Chlorogloeopsis sp. CALU 759 cells. Chlorophyll a fluorescence is shown with false-green (a); chlorophyll f fluorescence is shown with false-purple (b); phycocyanin fluorescence is shown with false-blue (c); chlorophyll a and chlorophyll f fluorescence (d); chlorophyll a and phycocyanin fluorescence (e); total pigment fluorescence (f). Scale bar, 5 μm.
In the case of FRL-grown Synechocystis sp. CALU 1173 (Figure 6), fluorescence topology was similar within the observed wavelength span. Namely, Chl f false-purple fluorescence (Figure 6(a)), Chl a false-green fluorescence (Figure 6(b)), PC false-blue fluorescence (Figure 6(c)), PE false-red fluorescence (Figure 6(d)), and pigment total fluorescence (Figure 6(e)) occupied cell periphery, and fluorescence channels mutually overlapped. The observed fluorescence topology matched with the electron microscopy data on peripheral thylakoid arrangement in Synechocystis spp. [69, 70].
Figure 6.
Laser confocal scanning micrographs of far-red light-grown Synechocystis sp. CALU 1173 cells. Chlorophyll f fluorescence is shown with false-purple (a); chlorophyll a fluorescence is shown with false-green (b); phycocyanin fluorescence is shown with false-blue (c); phycoerythrin fluorescence is shown with false-red (d); total pigment fluorescence (e). Scale bar, 2 μm.
6. Conclusion
Over past decades, FaRLiP photoacclimation has been studied in many respects, although in a small number of cyanobacterial strains [3]. In this connection, novel Chl f-producing strains Chlorogloeopsis sp. CALU 759 and Synechocystis sp. CALU 1173 would represent promising model objects. Importantly, although they belong to alternative morphotypes (trichome and single cell, respectively) and two distant phylogenetic lineages [71], fluorescence patterns of their FRL-grown cells similarly fall within the general FaRLiP response.
Acknowledgments
The authors are grateful to St. Petersburg University Research Center “Cultivation of microorganisms” (http://researchpark.spbu.ru/collection-ccem-rus/1628-ccem-kollekciya-calu-rus) for a help with strains maintenance. They also thank St. Petersburg University Research Centers “Molecular and Cell Technologies” and “Chromas” for research fellowship. The work was supported by the Russian Foundation for Fundamental Research, project no. 20-04-00020.
\n',keywords:"cyanobacteria, chlorophylls d and f, steady-state fluorescence detection, time-resolved fluorescence spectroscopy, confocal laser scanning microscopy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72456.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72456.xml",downloadPdfUrl:"/chapter/pdf-download/72456",previewPdfUrl:"/chapter/pdf-preview/72456",totalDownloads:526,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,introChapter:null,impactScore:0,impactScorePercentile:29,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"February 26th 2020",dateReviewed:"May 8th 2020",datePrePublished:null,datePublished:"September 30th 2020",dateFinished:"June 11th 2020",readingETA:"0",abstract:"The use of far-red light (FRL) is observed in some cyanobacteria, as well as in some marine and freshwater algae. While algae mobilize FRL absorbing antenna, which contains common chlorophyll a (Chl a), cyanobacteria produce red-shifted Chl d and/or Chl f. These pigments are synthesized either irrespective of ambient light or synthesized during FRL photoacclimation (FaRLiP), or adaptive remodeling of photosynthetic apparatus induced by relative enrichment with FRL quanta. The presence of red-shifted chlorophylls as well as their functions and topography are registered with various methods based on fluorescence measurement, such as: (1) steady-state fluorescence detection in live cells, cell fractions, and photosynthetic apparatus constituents; (2) time-resolved fluorescence spectroscopy, which traces energy transfer between individual pigments; (3) confocal laser scanning microscopy (CLSM), which helps to localize photosynthetic pigments in situ. This chapter describes photosynthetic apparatus in cyanobacteria and their photoacclimation phenomena. Over past decades, FRL photoacclimation has been studied in a small number of cyanobacteria. Novel Chl f-producing strains Chlorogloeopsis sp. CALU 759 and Synechocystis sp. CALU 1173 would represent promising model objects. Importantly, although they belong to alternative morphotypes and distant phylogenetic lineages, fluorescence pattern of their FRL-grown cells similarly falls within general FaRLiP response.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72456",risUrl:"/chapter/ris/72456",book:{id:"9294",slug:"fluorescence-methods-for-investigation-of-living-cells-and-microorganisms"},signatures:"Svetlana Averina, Ekaterina Senatskaya and Alexander Pinevich",authors:[{id:"318940",title:"MSc.",name:"Ekaterina",middleName:null,surname:"Senatskaya",fullName:"Ekaterina Senatskaya",slug:"ekaterina-senatskaya",email:"senatskaya.kate.vbg@yandex.ru",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Saint Petersburg State University",institutionURL:null,country:{name:"Russia"}}},{id:"318941",title:"Prof.",name:"Alexander",middleName:null,surname:"Pinevich",fullName:"Alexander Pinevich",slug:"alexander-pinevich",email:"a.pinevich@spbu.ru",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Saint Petersburg State University",institutionURL:null,country:{name:"Russia"}}},{id:"319196",title:"Associate Prof.",name:"Svetlana",middleName:null,surname:"Averina",fullName:"Svetlana Averina",slug:"svetlana-averina",email:"spb.svetik@rambler.ru",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Photosynthetic apparatus in cyanobacteria",level:"1"},{id:"sec_3",title:"3. Photoacclimation phenomena in cyanobacteria",level:"1"},{id:"sec_4",title:"4. Fluorescence methods employed in the research of far-red light-adapting cyanobacteria",level:"1"},{id:"sec_4_2",title:"4.1 Steady-state fluorescence detection",level:"2"},{id:"sec_5_2",title:"4.2 Time-resolved fluorescence spectroscopy",level:"2"},{id:"sec_6_2",title:"4.3 Confocal laser scanning microscopy (CLSM)",level:"2"},{id:"sec_8",title:"5. Far-red light photoacclimating strains in the CALU collection",level:"1"},{id:"sec_8_2",title:"5.1 Growth conditions",level:"2"},{id:"sec_9_2",title:"5.2 Steady-state fluorescence detection protocol",level:"2"},{id:"sec_10_2",title:"5.3 CLSM protocol",level:"2"},{id:"sec_11_2",title:"5.4 Steady-state fluorescence detection of chlorophylls and phycobiliproteins in white light- or far-red light-grown cells",level:"2"},{id:"sec_12_2",title:"5.5 CLSM of chlorophylls and phycobiliproteins in white light- and far-red light-grown cells",level:"2"},{id:"sec_14",title:"6. Conclusion",level:"1"},{id:"sec_15",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Miyashita H, Ikemoto H, Kurano N, et al. Chlorophyll d as a major pigment. Nature. 1996;383:402. DOI: 10.1038.383402a0'},{id:"B2",body:'Gan F, Bryant DA. Adaptive and acclimative responses of cyanobacteria to far-red light. Environmental Microbiology. 2015;17:3450-3465. 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1. Introduction
1.1 Mapping visual ambiguity and recognition
The ability to quickly and accurately recognise objects is a critical human skill – irrespective of whether one takes an evolutionary point of view (e.g. recognising a hidden snake amongst dense foliage) or one of daily life’s activities (e.g. locating one’s cup of coffee in a cluttered living room). Our visual system can interpret ambiguous visual input by grouping individual elements together and organising them into coherent and meaningful objects. Selecting relevant information and organising it into objects, relies on the sensory input as well as experience acquired through daily encounters with the visual world. Hence, stable and versatile object recognition requires both “bottom-up” sensory information and “top-down” influences such as prior knowledge, expectations, and goals. For instance, the famous picture of a Dalmatian dog (by photographer R. C. James) initially looks like a collection of random spots, but once one has seen the dog, it will pop out immediately any time the image is seen again. Hence, the percept changes dramatically, from ambiguous spots to a dog in a sun-spotted garden—even though the physical stimulus remains the same.
In this chapter, we will explore how cortical visual areas respond to computer-generated stimuli with perceptual qualities akin to the Dalmatian. Our goal was to gain insight into how neuronal responses change when the images are ambiguous, and when they are disambiguated by showing participants where and what the hidden object is. For the analysis, we combined population receptive field (pRF) mapping with blood oxygen level-dependent (BOLD) responses to chart spatially detailed maps of cortical activity in areas V1, V2 and V3.
1.2 Object recognition and the ventral pathway
Visual object recognition has traditionally been described as a largely hierarchical feedforward architecture, where visual information progresses from simple to more complex analyses in the ventral cortex [1, 2]. Many hierarchical models can be traced back to the seminal work of Hubel and Wiesel, who formulated a model where simple cells at the lower level of the hierarchy functioned as edge-detectors, while cells at higher levels pooled information by responding to specific patterns from the lower levels. Hence, there would be propagation towards increasingly complex responses. This feedforward architecture can also be found in the notion of a ventral visual pathway that gradually translates individual elements into whole objects, progressing from the primary visual cortex (V1) to the temporal lobe [3]. For instance, the fMRI literature mainly implicates the early visual areas in basic feature detection in V1 [4, 5, 6] grouping and crowding in V2 [7, 8], and figure-ground distinction in V2 [5, 9]. Whereas the LOC responds more during the recognition of object compared to other image categories or scrambled objects [4, 10, 11, 12, 13].
However, a purely feedforward view of object recognition may be too simplistic. Various studies have shown that the responses of early visual areas reflect a combination of input from the eyes, lateral interactions between neighbouring neurons, and recurrent signals from cortical and subcortical regions [14, 15, 16, 17]. This renders them an ideal seat for feedback-based mechanisms such as expectation (predictive coding, recurrence) and attention, which are potentially important components of human object recognition. Therefore, a deeper and more specific understanding of the roles of regions within the ventral pathway during object recognition is warranted. In the two following sections, we argue that prior expectations and attention shape the responses of V1, V2 and V3.
1.3 Expectations in object recognition
Predictive coding models propose that expectations act as a prior for object recognition, based on previous experiences in similar situations. The expectation is compared to the visual input, and the initial “guess’ is updated based on this comparison [18, 19]. Expectations regarding the presence of a specific object are reflected at early levels of the hierarchy where responses in V1 and V2 are modulated by context. For instance, one study found that activity increased in LOC and was reduced in V1 when simple lines were grouped into shapes [20], while another study, using Kanizsa triangles, found both increase in activity when the bottom-up input was different from the expectation of a receptive field and suppression when it was similar [21].
Thus, sensory input, expectations and prior knowledge are integrated with early visual areas.
Expectation and repetition of the same stimuli are typically reflected as an overall suppression of activity, which can be explained by two processes; either the expected parts are filtered out or there is a sharpening of tuning that enhances the activity of a selective population of neurons while decreasing that of the remaining neurons [22]. We may rely more on predictions when the sensory input is degraded or ambiguous compared to situations where visual stimuli are more easily recognised [22].
1.4 Object recognition in early visual areas: spatial-, feature- or object-based attention
Attention modulates the response of early visual areas to visual stimuli [23]. Attentional mechanisms are broadly divided into spatial-, object- and feature-based attention. In spatial-based attention, attention is directed towards a specific location of the visual field. For example, if a stimulus is always given on the same spot after a cue, a participant will focus more on that spot after the cue. Object-based attention is related to the presented stimulus. If a person gets the task to focus on a house stimulus in an image of two superimposed objects, they are using object-based attention [24]. In feature-based attention, attention is given to behaviourally relevant features, such as orientation, colour, or direction of movement [25]. In the early visual cortices, neurons selective to the attended features, i.e., orientation in V1, are more activated. This idea of attending to certain features of an object to enhance recognition has also been successfully implemented in the field of artificial intelligence [26]. Previous fMRI studies have shown that feature-based attention is measured as global changes across the whole visual field rather than at specific spatial locations [27, 28].
1.5 Current study
In this article, we take advantage of a new method to investigate object recognition with fMRI. We use emerging images, which are images composed of black and white patches, which hide an object [29], similar to the Dalmatian example mentioned previously. After extended viewing of at least several seconds, for most people, the hidden object will appear to emerge from the image. Essentially, EI delay recognition [30]. Importantly, while the physical visual information in the image does not change, the interpretation of the image changes dramatically upon its emergence.
With the help of EI, we aim to find whether early visual areas are involved in object recognition, and if so, in what way. Furthermore, we expect to find an increase in the LOC upon object recognition in the EI.
We present participants with an EI for 20 seconds, after which we aid recognition of the hidden object (disambiguation) by showing the silhouette of the hidden object. We then show the EI again and determined the differential BOLD responses of the early visual areas (V1, V2 and V3) and the higher-order LOC. To locate our ROIs, we used a retinotopic mapping paradigm for the early visual areas (V1-V3) and a scrambled images paradigm for the LOC. Early visual areas are retinotopically organised; neighbouring regions in the visual brain areas process neighbouring regions in the visual field [31]. We used the pRF parameters derived from the retinotopic mapping paradigm to back-project the BOLD response on the visual field, thereby creating visual coverage plots, which show us whether differences in BOLD response before and after disambiguation were local (around the edges of the object) or global. The edges of the object inform its location. Therefore, if the role of early visual areas in recognition of emerging images is an object- or spatial-based, we would expect to find stronger BOLD responses at the locations in the visual brain areas that cortically represent the edges of the object in the visual field. Alternately, if the role of early visual areas object recognition is feature-based, we expect a more global change in activity.
2. Methods
2.1 Data acquisition
Eight participants (age 21-–29 years, 2 male) participated in this study. Prior to scanning, all participants signed a consent form. The Medical Ethical Committee of the University Medical Centre Groningen approved the study. All participants reported normal or corrected-to-normal vision. Participants came in on three occasions: once for acquiring a high-resolution anatomical scan and running the retinotopic mapping paradigm, once for running the paradigm of the scrambled image, and once for running the EI paradigm. We scanned at the Neuroimaging Centre in Groningen, the Netherlands, using a Philips 3 Tesla MRI scanner (Philips, Best, the Netherlands). We obtained a high-resolution anatomical scan for each participant (TR = 9.00 ms, TE = 3.5 ms, voxel size = 1 x 1 x 1 mm). In addition, we acquired an in-plane scan before the retinotopic mapping and LOC paradigms to align the scan data with the high-resolution anatomical scan.
Stimuli were presented on a 24-inch BOLDscreen, an fMRI-compatible LCD screen with the dimensions of 620 x 445 mm and a refresh rate of 60 Hz. The resolution was set to 1920 x 1200 pixels. The distance between the subject and the screen was approximately 120 cm. Behavioural responses and eye movement data were collected using MATLAB with the Psychophysics toolbox 3 [32, 33] in combination with the Eyelink Toolbox [34].
2.2 Scanning paradigms
2.2.1 Retinotopic mapping
We performed retinotopic mapping to locate the V1, V2 and V3, and to project the BOLD response from the EI paradigm back onto the visual field. A bar with a radius of 10.21 degrees of visual angle and a width of 2.75 degrees of visual angle crossed the visual field of the participant 8 times in total, with 4 different orientations and 2 different motion directions [35]. We asked the participant to focus on a fixation dot in the centre of the screen and to press a button when this dot changed colour, to keep the visual field of the participant stable during the mapping. When the bar traversed the visual field, we inserted periods without a bar (‘blank periods‘) and used this in the model as a baseline. We presented the full paradigm six times to the participants while measuring BOLD responses with a repetition time (TR) of 1.5 s and an echo time (TE) of 30 ms. During these 6 scans, we collected 136 volumes of 24 slices with a voxel size of 2.33 x 2.33 x 3 mm, covering the visual cortex.
2.2.2 Intact and scrambled images
We determined the location of the LOC with the paradigm of the scrambled images. We showed participants intact images of objects and their “scrambled’ counterparts in an alternating fashion. To obtain the scrambled images, the grey-level intact object images were phase scrambled to preserve global image statistics but not local statistics necessary for recognition [12]. The mean luminance and intensity remained the same. While presenting the paradigm, we measured BOLD responses with a TR of 1.5 s and a TE of 30 ms. During this scan, we collected 215 volumes of 24 slices with a voxel size of 2.33 x 2.33 x 3 mm.
2.2.3 Emerging images (EI)
The emerging images were made via 3D models using the software [29]. We used 20 EI validated by Nordhjem et al. [30], including 5 images that did not contain a hidden object but had similar textures to those which did (no-object EI). 14 objects were animals, 1 was a person standing in a throwing position. The objects in the images were relatively large to avoid failure to recognise the object due to their small size (Figure 1).
Figure 1.
An emerging image (EI) used in this study. To see the hidden object of the EI highlighted, see appendix 1.
Before scanning, the participant viewed a sample trial on a laptop. We asked the participant to focus on a fixation dot in the centre of the screen. After a 10-second period of grey luminance (baseline, BL), the participant saw the EI for the first time for 20 seconds. The participant answered the question ‘Did you recognize an object?’ with “yes’ or ‘no’ with an MRI-compatible button box. A 20-second period of ‘free viewing’ came after this, where we showed the participant the same EI without a fixation dot. Subsequently, the participant was given four choices to indicate which (if any) object they had recognised, which consisted of three options (one of which was correct) and a ‘nothing’ option. We showed a silhouette of the hidden object of the same size and location as the hidden object to the participant for 8 seconds (disambiguation), to aid the recognition of the object [30]. For the no-object EI, we showed a silhouette of an object, even though the image did not contain this object. We showed a 10 seconds period of baseline again and asked the participant to focus on the fixation dot. Lastly, we showed the EI again for 20 seconds, while asking the participant to maintain fixation (Figure 2). To verify that the participants maintained a stable fixation, we viewed eye movements during the first and last presentations of the EI with an Eyelink 1000 eye tracker.
Figure 2.
Overview of one EI trial. When the image contains a fixation dot, participants were asked to fixate on this. EI1 shows the emerging image for the first time and EI2 for the last time. Between these two conditions, the emerging image was shown where participants can freely roam their eyes, and a silhouette of the hidden object is shown to encourage recognition (disambiguation). To determine whether recognition has already taken place before disambiguation, and whether the correct object was recognised, participants were asked to answer two questions concerning recognition of the first EI viewing via a button box.
The EI scan had a TR of 2 s and a TE of 30 ms. We collected 300 volumes of 36 slices with a voxel size of 1 mm isotropic. The EI paradigm consisted of 4 scans, with 5 EI trials per scan.
2.3 Analyses
2.3.1 Recognition performance
Participants were asked for input twice regarding the recognition of the EI. Every epoch, the participant answered the question “Did you recognise the object?’ after the first viewing of the EI, and ‘What object did you recognize?’ after the disambiguation. If either or both responses were not recorded (due to a malfunction in the button-box recording or the participant not answering), we label this as No Response. Other combinations are labelled Correct Recognition, Incorrect Recognition, or No Recognition (Table 1). If the object was recognised during the first showing of the EI, this will have taken time. Therefore, if the object is recognised at any time during the epoch, the period before disambiguation has, per definition, less time in which the object is recognised than after disambiguation. For this reason, we combined the responses with the correct multiple-choice answer, regardless of the answer to the first yes-or-no question. No-object EI and EI with too much motion were not included in this analysis.
Recognition after first EI
Multiple choice after disambiguation
Label
Frequency
Yes
Correct
Correct recognition
18%
Yes
Incorrect
Incorrect recognition
27%
No
Correct
Correct recognition
16%
No
Incorrect
No recognition
15%
No Response(s) Recorded
No response
24%
Table 1.
Possible combinations of responses regarding the EI recognition, and the frequency in which they occurred. If the participant did not answer one or both questions, or the answer was not recorded, this was classified as “No response’.
We compared models with and without Recognition as a fixed factor with a theoretical likelihood ratio test. If the 95% Confidence Interval (CI) of the EI-deltas did not include zero, we considered the result to be significant.
2.3.2 Pre-processing
We aligned the T1-weighted anatomical scans to the AC-PC space. Segmentation of the brain was done automatically via Freesurfer (http://surfer.nmr.mgh.harvard.edu/) with additional manual corrections with ITK-snap (http://www.itksnap.org/) [36],). The retinotopic scan included a 12-second pre-scan, so the first 8 volumes were discarded. Motion correction (both between and within scans) was done with mrVista (http://vistalab.stanford.edu/software/) in MATLAB (version 2017b, MathWorks, Natick, MA, USA). We excluded scans with a motion greater than 3 voxels from analysis. We aligned the functional data to the anatomical data for the retinotopic paradigm and scrambled images paradigm with FSL (http://fsl.fmrib.ox.ac.uk/fsl/) [37]. Due to a lack of an in-plane scan for the EI paradigm, we manually aligned the EI data to a mean image of all functional scans. We interpolated the functional data to grey matter (4 layers) using the anatomical grey-white matter segmentation.
We averaged the six retinotopic scans to increase its signal-to-noise ratio. In case of missing volumes, we clipped all scans to fit the lowest number of volumes before averaging, or we averaged the scans in two groups; one group with the usual 136 volumes and one with the scans that contained less.
One participant did not participate in the LOC localiser scan, leaving 7 participants for the LOC analysis. All participants participated in the EI scan, but two participants moved too much (more than 3 voxels) during the scan, resulting in the exclusion of 5 EI images (of which 2 no-object EI) in one participant and 15 (of which 4 no-object EI) in another.
2.3.3 pRF analysis
We modelled population receptive field (pRF) parameters, i.e. pRF eccentricity, polar angle and size, from the BOLD responses during the retinotopic scan. The pRF analysis calculated the predicted BOLD signal with the use of a 2D Gaussian model [35]. We varied the spread (sigma) and location (x, y) to find the best fit (minimum residual sum of squares between the predicted response and the data) for each voxel. The pRF model convolved with the stimulus matrix and BOLD hemodynamic response function (HRF) to compute the fMRI time series for each voxel. The best model fit provided us with a preferred location of the visual field (expressed in eccentricity and polar angle parameters), a pRF size and a percent variance explained by the fit for each voxel time course and the pRF model (Figure 3).
Figure 3.
Overview of the analysis steps. The pRF analysis (left panel) was conducted to define the early visual areas for each participant and to obtain the pRF location and visual field coverage for each voxel. V1 ROI is shown in the figure. A GLM analysis (right panel) was performed to model BOLD responses to the stimuli per voxel. Finally, BOLD activity within the ROI was projected on the visual stimulus using pRF coordinates and size (i.e. VF coverage).
2.3.4 Definition of V1, V2 and V3
We used the polar and eccentricity parameters obtained from the pRF analysis to delineate the early visual areas (V1, and a dorsal and ventral V2 and V3) on the inflated cortical surface [35, 38]. The borders of the ROI were based on the phase reversals in the polar angle map and an eccentricity value of max. 10.2 degrees of visual angle. We combined ventral and dorsal parts of V2 and V3 for analysis (Figure 4B). The threshold for variance explained of the data was set to 0.15.
Figure 4.
A. Right LOC ROI drawn on an inflated cortex of a representative participant, localised with the help of the paradigm of the scrambled image and with the study of Vinberg and Grill-Spector [39] as leading reference. Red areas indicate a significantly higher activation towards intact images than scrambled images (p < 0.01). B. Right hemisphere ROIs V1, V2 and V3 were drawn on an inflated cortex of a representative participant. Left and right parts of the ROI were combined so that each ROI covered the complete visual field. Colours represent the polar angle to which the region reacts the most.
2.3.5 Definition of the LOC
We used a general linear model (GLM) to contrast the BOLD responses towards the intact images with the scrambled images. We delineated the LOC ROIs on an inflated cortical surface based on the stronger response towards intact images (p < 0.01) and the rough location of object-sensitive areas [39, 40, 41, 42], where the paper of Vinberg and Grill-Spector [39] formed the primary example (Figure 4A). When LOC ROIs overlapped with V1, V2 or V3, early visual areas took precedence and the LOC ROIs were adjusted to remove the overlap.
2.3.6 Visual field projection
During the retinotopic scan, a bar that crossed the visual field was shown. The location of the bar in the visual field (say, location A) activated voxels in the visual cortex (say, voxel 1 and voxel 2). Vice versa, if these voxels 1 and 2 showed activation in the EI paradigm, we know its corresponding location in the visual field, i.e. location A. This procedure, in combination with knowing the pRF parameters such as size and variance explained, enabled us to back-project the BOLD responses to the EI to corresponding locations in the visual field. The 2D Gaussians that represent the pRF’s of the ROI were added together, weighted by a voxel’s BOLD response. This resulted in a coverage plot (in visual space) both before and after disambiguation, for the V1, V2 and V3. Voxels with variance explained below 0.25 and/or an eccentricity greater than 1.5 times the field of view range (1.5 x 10.21 = 15.32 degrees of visual angle) were not included in the reconstruction of the visual field.
To counteract the possibility that a region in the visual field shows a higher EI-delta because it is covered by a large number of pRFs (mostly due to foveal bias), we added the BOLD response of the voxels per pixel and divided this by the sum of the voxels per pixel where all voxels had a value of 1 [43].
2.3.7 EI-delta
To find the difference between the activation towards the EI before and after disambiguation, we subtracted the activation of the first EI viewing from the second, creating an ‘EI-delta’. We used MATLAB (version 2020a, MathWorks, Natick, MA, USA) to fit a linear mixed-effects analysis with a General Linear Model (GLM), for each ROI, on the BOLD response before and after disambiguation. As random factors, we included intercepts for subjects and type of EI (e.g. bunny, cow, etc.).
Individual differences and differences between EI in maximum BOLD responses make it difficult to compare back-projected coverage plots between individuals. This is why we ‘normalised’ the EI-delta before the back-projection; we divided the difference in BOLD response by the sum of BOLD response towards the EI before and after disambiguation. We did this for each person and each EI individually.
2.3.8 BOLD activity as function of distance to nearest edge
To look at activity across visual field projections, we plotted BOLD activity as a function of visual field distance to the nearest edge of the object. This approach made it possible to combine the EIs even though the hidden object was placed in different locations. We defined the edges of the object with the 3D models that created the EI [29]. The distance to the nearest edge was defined as the distance to the closest edge of the 3D model, with negative values outside the object and positive inside the object. We plotted the median and interquartile ranges of the EI-delta across all EI and all subjects.
3. Results
In summary, the BOLD response after disambiguation was stronger than before, in both V1 and V2. The strongest increase was found in V1. We did not observe this effect in the LOC or V3. When back-projected onto the visual field, the EI-delta showed a global pattern, rather than a spatially localised one. The distance-to-edge analysis showed no relation between EI-delta and distance to the edge of the object.
3.1 Recognition influence
EI recognition responses were distributed as follows: Correct Recognition 34% (36/106), Incorrect Recognition 27% (29/106), No Recognition 15% (16/106) and No Response 24% (25/106) (Table 1). We tested all statistical models with and without the influence of Recognition. In all ROI, the models without Recognition as a fixed factor proved to be better than the models that included Recognition, indicating that Recognition did not have a significant influence on the EI-delta (V1: Likelihood Ratio Test χ2(1) = 5.57, p = 0.13, V2: Likelihood Ratio Test χ2(1) = 1.93, p = 0.59 and V3: Likelihood Ratio Test χ2(1) = 1.93, p = 0.59).
3.2 Early visual areas – Before and after disambiguation
The best-fitting models for V1 and V2 had intercepts above zero (V1: 0.36, V2: 0.15) with 95% Confidence Intervals above zero as well (V1: 0.26 0.46, V2: 0.06 to 0.24). This indicates that the EI-delta values for V1 and V2 were significantly higher than 0. The EI-delta values of V3 did not differ significantly from 0 (intercept: 0.06, CI: −0.05 to 0.16) (Table 2, Figure 5).
ROI
Best model
Intercept
SE
Lower CI
Upper CI
V1*
DeltaV1 ∼ 1 + (1|EItype) + (1|Subject)
0.36
0.05
0.26
0.46
V2*
DeltaV2 ∼ 1 + (1|EItype) + (1|Subject)
0.15
0.04
0.06
0.24
V3
DeltaV3 ∼ 1 + (1|EItype) + (1|Subject)
0.06
0.05
−0.05
0.16
LOC
EIdelta∼1 + (1|EItype) + (1|Subject)
−0.06
0.05
−0.15
0.03
Table 2.
The best-fitting linear mixed-effects model for all ROI, with corresponding intercept, standard error (SE) and lower and upper limits of the confidence interval of 95% (CI). ROI V1 and V2 had an EI-delta that was significantly higher than 0, as indicated by the asterisk.
Figure 5.
Boxplot showing the EI-delta of the ROIs. Boxes indicate the 25–75 percentiles, lines inside the boxes indicate the median. Dots represent individual data points: One EI of one participant.
3.3 LOC – Before and after disambiguation
Recognition and lateralization did not contribute significantly to the model (Likelihood Ratio Test χ2(1) = 3.19, p = 0.36 and χ2(1) = 0.002, p = 0.97, respectively). The 95% confidence interval includes zero (−0.15 to 0.03), so the EI-delta of the LOC did not differ significantly from zero (Table 2, Figure 5).
3.4 Early visual areas: visual field projection
A visual inspection of the coverage plots showed no pattern, either within participants over EI or within EI over participants (Figure 6). Also, there was no clear visual difference in EI-delta coverage plots when participants were shown stimuli that contained an object and when they saw stimuli where there was no hidden object (an example is shown in the appendix, A2).
Figure 6.
Coverage plots of V1, V2 and V3 for the cow EI (a) and bunny EI (B). Each circle represents the visual field of a participant with a radius of 10.2 degrees of visual angle. EIs on the same column represent the same individual. The rows represent V1, V2 and V3. Colours indicate the difference in BOLD response before and after disambiguation (EI-delta). Although the depicted range of EI-delta is between -2x10−5 and 2x10−5, EI-deltas sometimes showed higher or lower values than this range. This is plotted as deepest red or deepest blue, respectively.
We did see a similar pattern within participants, within EI, over ROI. The EI-delta of V1, V2 and V3 towards, for example, the cow EI show higher and lower activation at similar locations of the visual field. A clear example of this is the second-to-last column of Figure 6A. This participant shows an increase in activation after disambiguation in the upper left corner of all three ROIs, albeit in different amounts.
3.5 Early visual areas: Distance to edge
Median EI-delta of V1, V2 and V3 was plotted against distance to edge across all participants and EI (Figure 7). Each plot shows a relatively flat line close to zero, indicating very little difference before and after disambiguation regardless of whether the BOLD activity is measured inside the object, on the edge or outside the object. Thus, the spatial location of the object is not related to changes in BOLD activity after disambiguation. This supports the outcome of the coverage plots that also did not indicate patterns of changes in activity at specific spatial locations.
Figure 7.
Delta-EI contrast effect size (a.u.) plotted as a function of distance to the nearest edge for V1 (a), V2 (B) and V3 (C). The shaded areas represent interquartile range (darker shade) and the 90% confidence interval (lighter shade).
4. Discussion
4.1 Ventral pathway: no increased activity in “object area” LOC after disambiguation
Studies regarding illusory contours indicate the likely involvement of the LOC [44]. However, our results do not support this. If the LOC facilitates the change in activity in lower-order areas via feedback, we expect to see a significant difference in BOLD signal in the LOC after disambiguation. A possible explanation for this is that the LOC might already be active before the disambiguation. Perhaps the LOC reacts before the conscious recognition process takes place. This is in line with the eye-tracking study of Nordhjem et al. [30], which showed that participants made more fixations near the edges of the object almost immediately after the EI was presented and well before recognition took place. Visual areas might respond to another structure in the patterns on the object before or even without conscious recognition. If this is the case, we need to re-evaluate the LOC as an “object recognition’ area.
Another reasoning for the lack of change in the LOC could be repetition suppression. Repetition suppression is a lower BOLD response when a participant views the same stimulus more than once and is usually stronger in higher-order areas [45, 46, 47, 48]. If the lack of significant results in the LOC is indeed caused by the counteraction of the effect of repetition suppression, this raises an interesting issue: does the LOC regard the EI before and after disambiguation as the same stimulus? If so, this implies that the recognition of the object makes no difference to the interpretation by the LOC. Either way, the lack of difference in BOLD response in the LOC does not indicate a lack of involvement. Many studies have indicated the importance of the LOC in object recognition [4, 10, 11, 12, 13] and we do not challenge the outcomes of those studies.
4.2 Ventral pathway: higher activation in V1 and V2 but not in V3 after disambiguation
The scrambled images paradigm suggests no involvement of V1 and V2, since the activation of these areas is not higher towards intact compared to scrambled images [49]. However, the EI paradigm contradicts this. The higher activity towards the EI after disambiguation shows that V1 and V2 might also be involved in object recognition, either directly or via feedback (e.g., expectations).
In order to recognise the object in the EI, the participant needs to perceive an edge that is not physically present – an illusory contour (IC). V2 is seen as an area that facilitates figure-ground distinction and detection of IC [9, 44, 50]. Thus, the increased activation of V2 is in line with previous literature. Although the role of V1 in edge detection is less clear [9, 44, 50], we found a significant change in activity here as well.
Disambiguation did not affect mean activity in V3. This supports the idea that V3 is less involved in the ventral pathway [5, 51]. Although the overall activation of the V3 showed no significant difference, this does not mean that no change in activity occurred. Looking at the coverage plots of V3, we see spots of increased and decreased activation similar to V1 and V2, although to a lesser extent.
4.3 Feature-based attention
The visual coverage plots and the distance to edge analysis showed no correspondence of activity to the object locations. This lack of pattern in the maps could reflect substantial variation in the underlying neural recognition processes and strategies across participants and images. Compared to other stimuli, e.g. ones inducing illusory contours such as Kanizsa figures and oriented line segments [21, 52], EIs are more complex and perceptually stand out less clearly. Because EIs are perceptually challenging, this could eventually lead to weaker or noisier responses following recognition. Moreover, some images may not have been sufficiently recognisable for some participants.
However, we did see an overall increase in activity for EI-delta in V1 and V2, which begs the question, what information is then enhanced? One possible explanation is that the role of the early visual areas is not related to a specific spatial location, i.e. enhancement or suppression of activity in voxels in the proximity of the object. Instead, there could be enhancement of specific features in the whole visual field. This interpretation would be in line with previous studies of feature-based attention [27, 28].
4.4 Limitations and future studies
Our study included a relatively small participant population and rather complex stimuli. Hence, the participants may have varied in their ability to recognise the images and the ways they did it. In general, the mechanisms behind visual information processing are similar in people without visual impairments and should be possible to study even in a limited population. However, future studies with larger sample sizes or different groups of participants could reveal more individual differences.
We compared the first and last viewing of the EI under the assumption that the EI would be disambiguated by the time of the second presentation. The assumption that EI can be disambiguated is supported by our previous study that showed a recognition performance of median = 100% (Inter quartile range 93.3–100%) if an EI is primed by an object with the same shape as the one that is hidden in the EI [30]. However, we cannot be certain that the recognition occurred during the disambiguation. The EI could have been recognised before the disambiguation or could have remained unrecognised after the disambiguation. This can be circumvented in future replication studies by asking the participant to indicate the point of recognition during the viewing of the EI instead of reporting after stimuli presentation. However, assuming that object recognition occurs at some point within the experiment, the EI after disambiguation will automatically contain more time where the object is recognised than the EI before disambiguation. Thus, comparing the second viewing of the EI to the first still provides us with the changes related to recognition, albeit not as precisely as we wanted. Even with this uncertainty, we see a significant difference, indicating a clear involvement of the V1 and V2 in object recognition.
All the objects were different shapes and sizes, which made our stimuli more complex than previous studies that used geometrical shapes [21, 52]. For that reason, the analysis of BOLD activity projected in visual field coordinates could only be carried out for one EI at a time. Straightforward averaging across stimuli was not possible, although plotting activity as a function to the nearest edge gave some approaches to combine data across EI. Furthermore, as participants were required to fixate on the centre of the image, the eccentricity at which the object was located could play a role in this. There is a higher density of smaller-sized pRFs near the fovea, whereas these become larger and somewhat sparser at higher eccentricity. Thus, in future experiments, it could be advantageous to present the objects at a consistent location and at the same eccentricity. This might facilitate drawing conclusions from multiple stimuli and avoid differences related to the density or reliability of the various pRFs.
5. Conclusion
This study showed that, in our participant group, disambiguation of EI significantly alters the activity in the V1 and V2. This supports the idea that object recognition does not depend on processes in one particular area, but is a result of interplay between various levels. Furthermore, we did not find a pattern in visual field coordinates of this change in activation. We here speculate that the roles of V1 and V2 in object recognition rely on feature-based rather than spatial-based attention.
Our EI study uses novel stimuli and combines retinotopic mapping with BOLD activity and thereby provides a more detailed view of the changes in activity within regions. These results could not have been achieved by standard paradigms (i.e., scrambled and intact objects). Thus, it adds new insight to the existing studies of object recognition and new tools in computational visual neuroscience.
Acknowledgments
BN was supported by a grant from the Netherlands Organisation for Scientific Research (NWO Brain & Cognition grant 433-09-233) to FWC. NG was supported by a scholarship from the (Chilean) National Commission for Scientific and Technological Research. The authors would like to thank Dr. Ben Harvey for his advice on how to apply and modify the population receptive field method. We also thank Dr. Niloy J. Mitra, and Dr. Hung-Kuo Chu for sharing the program that was used to generate the emerging images and for their help with the stimuli creation.
Figure A1.
An Emerging Image (EI) used in this study (left) and the object that is hidden in it is highlighted (right).
Figure A2.
Coverage plots of V1, V2 and V3 for one of the images without a hidden object. Each circle represents the visual field of a participant with a radius of 10.2 degrees of visual angle. EI’s on the same column represent the same individual as shown in Figure 6.
\n',keywords:"object recognition, early visual areas, lateral occipital complex, emerging images",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82953.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82953.xml",downloadPdfUrl:"/chapter/pdf-download/82953",previewPdfUrl:"/chapter/pdf-preview/82953",totalDownloads:1,totalViews:0,totalCrossrefCites:0,dateSubmitted:"February 15th 2022",dateReviewed:"June 7th 2022",datePrePublished:"August 5th 2022",datePublished:null,dateFinished:"August 5th 2022",readingETA:"0",abstract:"Human observers can reliably segment visual input and recognise objects. However, the underlying processes happen so quickly that they normally cannot be captured with fMRI. We used Emerging Images (EI), which contains a hidden object and extends the process of recognition, to investigate the involvement of early visual areas (V1, V2 and V3) and lateral occipital complex (LOC) in object recognition. The early visual areas were located with a retinotopy scan and the LOC with a localiser. The participants (N=8) then viewed an EI, followed by the hidden object’s silhouette (disambiguation), and then, the EI was repeated. BOLD responses before and after disambiguation were compared. The retinotopy parameters were used to back-project the BOLD response onto the visual field, creating spatially detailed maps of the activity change. V1 and V2 (but not V3) showed stronger response after disambiguation, while there was no difference in the LOC. The back-projections revealed no distinct pattern or changes in activity on object location, indicating that the activity in V1 and V2 is not specific for voxels corresponding to the object location. 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Cornelissen and Barbara Nordhjem",book:{id:"11374",type:"book",title:"Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex",subtitle:null,fullTitle:"Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex",slug:null,publishedDate:null,bookSignature:"Dr. Alyssa A Brewer and Dr. Brian Barton",coverURL:"https://cdn.intechopen.com/books/images_new/11374.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-649-9",printIsbn:"978-1-80355-648-2",pdfIsbn:"978-1-80355-650-5",isAvailableForWebshopOrdering:!0,editors:[{id:"115304",title:"Dr.",name:"Alyssa",middleName:"A",surname:"Brewer",slug:"alyssa-brewer",fullName:"Alyssa Brewer"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. 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Proceedings of the. National Academy of Sciences. 2002;99(23):15164-15169. DOI: 10.1073/pnas.192579399'},{id:"B21",body:'Kok P, de Lange FP. Shape perception simultaneously up- and downregulates neural activity in the primary visual cortex. Current Biology. 2014;24(13):1531-1535. DOI: 10.1016/j.cub.2014.05.042'},{id:"B22",body:'De Lange FP, Heilbron M, Kok P. How do expectations shape perception? Trends in Cognitive Sciences. 2018;22:764-779. DOI: 10.1016/j.tics.2018.06.002'},{id:"B23",body:'Roelfsema PR, de Lange FP. Early visual cortex as a multiscale cognitive blackboard. Annual Review of Vision Science. 2016;2:131-151. DOI: 10.1146/annurev-vision-111815-114443'},{id:"B24",body:'Ciaramitaro VM, Mitchell JF, Stoner GR, Reynolds JH, Boynton GM. Object-based attention to one of two superimposed surfaces alters responses in human early visual cortex. Journal of Neurophysiology. 2011;105(3):1258-1265. DOI: 10.1152/jn.00680.2010'},{id:"B25",body:'Maunsell JH, Treue S. Feature-based attention in visual cortex. Trends in Neurosciences. 2006;29(6):317-322. DOI: 10.1016/j.tins.2006.04.001'},{id:"B26",body:'Lindsay GW. Feature-based attention in convolutional neural networks. arXiv preprint arXiv:1511.06408. 2015'},{id:"B27",body:'Saenz M, Buracas GT, Boynton GM. Global effects of feature-based attention in human visual cortex. Nature Neuroscience. 2002;5(7):631-632. DOI: 10.1038/nn876'},{id:"B28",body:'Serences JT, Boynton GM. Feature-based attentional modulations in the absence of direct visual stimulation. Neuron. 2007;55(2):301-312. DOI: 10.1016/j.neuron.2007.06.015'},{id:"B29",body:'Mitra NJ, Chu H-K, Lee T-Y, Wolf L, Yeshurun H, Cohen-Or D. Emerging images. ACM Transactions on Graphics. 2009;28(5):1-8. DOI: 10.1145/1618452.1618509'},{id:"B30",body:'Nordhjem B, Kurman Petrozzelli CI, Gravel N, Renken RJ, Cornelissen FW. Eyes on emergence: Fast detection yet slow recognition of emerging images. Journal of Vision. 2015;15(9):8. 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University Medical Centre Groningen, University of Groningen, Laboratory for Experimental Ophthalmology, The Netherlands
University Medical Centre Groningen, University of Groningen, Laboratory for Experimental Ophthalmology, The Netherlands
Department of Paediatrics and Adolescent Medicine, Copenhagen University Hospital, Denmark
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Our business values are based on those any scientist applies to their research. The values of our business are based on the same ones that all good scientists apply to their research. We have created a culture of respect and collaboration within a relaxed, friendly, and progressive atmosphere, while maintaining academic rigour.
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Openness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
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Disruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
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What makes IntechOpen a great place to work?
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IntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
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If this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
\r\n
\r\n\t
\r\n
\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
\r\n
\r\n\t
\r\n
\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
\r\n
\r\n\t
\r\n
\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
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Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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