\r\n\tThis book aims to present an overview of the current status of nanofibers, fabrication and recent trends in the fabrication of nanofibers, and functional nanofibers and applications of nanofibers in various fields including environmental, bio-sensing, drug delivery, catalysis, and medical. The book hopes to provide a piece of up-to-date information about the mentioned topics and fundamental knowledge necessary for the advanced study in the field of nanofibers and their applications, making it interesting to research students, scientists, engineers, and material scientists.
",isbn:"978-1-80356-387-9",printIsbn:"978-1-80356-386-2",pdfIsbn:"978-1-80356-388-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"a255898117275990dffe83c75a9f815d",bookSignature:"Dr. Maaz Khan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11462.jpg",keywords:"Nanofiber, Nanofiber Fabrication, Functional Nanofiber, Nanofiber Application, Fiber Technology, Electrospinning, Drug Delivery, Fabrication Strategy, Commercialization, Polymer, Tissue Engineering, Catalysis",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 23rd 2022",dateEndSecondStepPublish:"April 26th 2022",dateEndThirdStepPublish:"June 25th 2022",dateEndFourthStepPublish:"September 13th 2022",dateEndFifthStepPublish:"November 12th 2022",remainingDaysToSecondStep:"23 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Maaz Khan is an expert in the field of Nanoscience and Nanotechnology with over 100 articles and 3,300 citations to his name.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"107765",title:"Dr.",name:"Maaz",middleName:null,surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan",profilePictureURL:"https://mts.intechopen.com/storage/users/107765/images/system/107765.png",biography:"Dr. Maaz Khan is working as Deputy Chief Scientist (Professor) at PINSTECH, Pakistan. He has done Ph.D. and post doctorate in the field of Material Science (Nanoscience). His research interests include fabrication of nanomaterials and their structural, optical, magnetic, and electrical characterizations. He has authored more than 100 research articles and published 10 books. Presently, he is the Editor-in-Chief of ‘Journal of Materials, Processing and Design\\' and \\'The Nucleus\\'. He is also the Executive Editor of \\'International Journal of Nano Studies and Technology\\'. 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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5404",title:"Raman Spectroscopy and Applications",subtitle:null,isOpenForSubmission:!1,hash:"7d447d2811c5d3fc696761bb12fe3166",slug:"raman-spectroscopy-and-applications",bookSignature:"Khan Maaz",coverURL:"https://cdn.intechopen.com/books/images_new/5404.jpg",editedByType:"Edited by",editors:[{id:"107765",title:"Dr.",name:"Maaz",surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4644",title:"The Transmission Electron Microscope",subtitle:"Theory and Applications",isOpenForSubmission:!1,hash:"6ef878a14961b97ec0bc5c1762a46aa0",slug:"the-transmission-electron-microscope-theory-and-applications",bookSignature:"Khan 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Applications",subtitle:null,isOpenForSubmission:!1,hash:"da3cb0d978d197ed95c07e8090e06136",slug:"modern-spectroscopic-techniques-and-applications",bookSignature:"Maaz Khan, Gustavo Morari do Nascimento and Marwa El-Azazy",coverURL:"https://cdn.intechopen.com/books/images_new/7674.jpg",editedByType:"Edited by",editors:[{id:"107765",title:"Dr.",name:"Maaz",surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10941",title:"Ferrites",subtitle:"Synthesis and Applications",isOpenForSubmission:!1,hash:"f6a323bfa4565d7c676bc3733b4983b0",slug:"ferrites-synthesis-and-applications",bookSignature:"Maaz Khan",coverURL:"https://cdn.intechopen.com/books/images_new/10941.jpg",editedByType:"Edited by",editors:[{id:"107765",title:"Dr.",name:"Maaz",surname:"Khan",slug:"maaz-khan",fullName:"Maaz Khan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5884",title:"Unraveling the Safety Profile of Nanoscale Particles and Materials",subtitle:"From Biomedical to Environmental Applications",isOpenForSubmission:!1,hash:"5e5811aa0f15ab9d8b6a235e8408875d",slug:"unraveling-the-safety-profile-of-nanoscale-particles-and-materials-from-biomedical-to-environmental-applications",bookSignature:"Andreia C. Gomes and Marisa P. Sarria",coverURL:"https://cdn.intechopen.com/books/images_new/5884.jpg",editedByType:"Edited by",editors:[{id:"146466",title:"Prof.",name:"Andreia",surname:"Ferreira de Castro Gomes",slug:"andreia-ferreira-de-castro-gomes",fullName:"Andreia Ferreira de Castro Gomes"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"70585",title:"Introductory Chapter: Blockchain Technology and Smart Healthcare",doi:"10.5772/intechopen.90633",slug:"introductory-chapter-blockchain-technology-and-smart-healthcare",body:'\nPhase 1 of the technology revolution began with the development of the computer and the computer chip. Phase 2 occurred when communication between computers became widely distributed via the Internet. We are now in phase 3 which is the development of artificial intelligence to replace or augment human intelligence. This revolution in technology has and will continue to have profound effects upon healthcare, human health, and longevity.
\nBlockchain technology has played a major role in the ongoing development of artificial intelligence. In the financial world, it has created a new, Internet-based global monetary system through the development of the Bitcoin protocol [1]. The ethereum blockchain has made the use of Turing complete computer programs available online, enabling the development of smart contracts [2, 3]. To complete the transition to a smart economy, the blockchain-based, self-sovereign smart identities are being developed [4, 5]. Taken together, these blockchain-based developments have created the foundation for a truly decentralized healthcare system in which patients have control of their own medical data by the use of a digital identity, and providers are reimbursed with digital money (bitcoin) immediately after providing documentation of services rendered (via ethereum-based smart contracts).
\nWhile promising, blockchain technology remains in its infancy, and there continues to be a need to clarify what exactly blockchain technology involves. To do this, a review of the original Bitcoin white paper is required. This white paper was published under the pseudonym “Satoshi Nakamoto.” It is unknown whether this pseudonym represents a single person or group of people, as the author as of 2019 remains unknown. The original Bitcoin codebase and white paper were written in 2008 [1, 6]. The first bitcoin was mined by Satoshi Nakamoto on January 3, 2008, and the first bitcoin transaction occurred shortly thereafter between Satoshi Nakamoto and Hal Finney [7].
\nThe blockchain is simply a distributed database that is populated with transactions. Given the nature of distributed blockchain databases, the transactions are immutable and permanent. Furthermore, advanced computer cryptography has made it possible to fully anonymize transactions using zero-knowledge proofs [8]. The transactions recorded can be many different things, including financial transactions using bitcoin, smart contracts using ethereum, Internet advertisements [9], or any of a number of healthcare-specific transactions. The immutable, permanent nature of blockchains makes healthcare applications particularly useful in maintaining patient records, billing insurances, and conducting medical research [10].
\nThe revolutionary aspect of blockchain technology is that it eliminates the need for a trusted third party to validate transactions and store data. With current electronic medical records, for example, access to medical records is controlled by the clinic or hospital. Individuals must ask the hospital for access to their own medical records. Medical records are stored by the hospital in a centralized location. This centralization of data storage creates a single point of entry for hackers [11].
\nDistributed blockchain databases, on the other hand, do not have a single vulnerable point of access, making them extremely resistant to malicious manipulation. For example, although Internet sites managing bitcoin have been hacked, the bitcoin blockchain has not. This is in spite of a $125 billion USD market capitalization. Theoretically, a hacker able to break the bitcoin blockchain would have access to billions of dollars. In spite of this large financial incentive, after more than a decade of existence publicly on the Internet, the bitcoin blockchain remains intact.
\nNot only does blockchain technology enable storing data in an immutable, permanent way, it also can store contracts in a similar way. Such contracts can be “dumb” and simply record an agreement, or they can be “smart” and take action. For example, a smart contract could automatically release funds from an insurance company, payable directly to the provider, immediately when proper documentation of medical services rendered was uploaded to the blockchain. Such transactions are enabled by having a blockchain-based digital identity for patients, digital assets for insurance company payments, and digital smart contracts. An integrated blockchain including all three of these components is NEO, originally founded in China, which aims to create the essential components of blockchain-based economy [12, 13].
\nSmart contracts, along with digital identities, enable the implementation of artificial intelligence in remote patient monitoring, hospital wards, and clinics. A smart contract could simply be the instruction to notify a patient’s provider and/or possibly emergency medical services when the patient’s wearable technology identifies new onset atrial fibrillation. The use of blockchain technology as opposed to a centralized database helps ensure data integrity, patient privacy, and database robustness.
\nBy making artificial intelligence patient centric, blockchain technology is leading the way through the growing wave of technology and artificial intelligence. By creating self-sovereign digital identities, people will have control over their identification instead of relying on government institutions for identification. This is critically important, especially for refugees and the homeless. By creating several methods of transferring assets, blockchain technology allows people to trade directly with each other, without relying on a central bank or credit card company to audit their every transaction. Such monetary systems are essential for the creation of efficient smart contracts that do not rely excessively on a centralized verification process. Finally, blockchain-based smart contracts have the ability to efficiently analyze patient data and act quickly on the results.
\nThe Amazon rainforest, well known for its vast biodiversity, is a unique ecosystem and plays an irrefutable role in the maintenance of global ecosystem services. The Amazon biome is one of the main contributors to the biogeochemical functioning of the terrestrial system [1]. A contributor to this functioning is the soil, which is considered one of the most complex and variable environmental compartments. The understanding of microbially mediated biogeochemical processes in this compartment is of particular interest in continental floodplains, where nutrient cycling is highly responsive for floating hydrology, and the gases produced in the soil may influence the global climate change [2].
Methane (CH4) is one of the most important greenhouse gases (GHG). Amazonian wetlands are considered a significant source of CH4 emissions. In addition to the positive emission of GHG such as CH4, the wetlands provide a diverse range of vegetation that enables the sequestration of organic carbon.
Arguably, the most striking variation in the nature of the forests in the Amazon floodplains system is related to seasonal flooding. Variations in the level and flow of water, along with variations in temperature and sediment load, for example, are the most important factors to guide the structure and functioning of flood systems [3, 4]. The flood pulse, responsible for the change in soil saturation, makes those areas predisposed to the activity of the anaerobic microbial community. In the absence of electron acceptors, CH4 is the final product of anaerobic decomposition of organic matter. The anaerobic oxidation of CH4 may occur in the presence of some of these electron receptors (iron, manganese, and sulfate) and nitrogenous forms (nitrite and nitrate).
Given the recent recognition of the importance of Amazonian floodplains for the global dynamics of the CH4 cycle, we emphasize the essential role of information from these systems as a key factor for the amelioration of CH4 emission models. Thus, microbiota data combined with
Global biogeochemical cycles are mainly driven by microorganisms that feed on base compounds of carbon (C), such as CH4 or carbon dioxide (CO2) [5]. The CH4 is the most abundant hydrocarbon in the atmosphere [6]. Due to its absorption characteristics, CH4 manifests positive radiative forcing, being a GHG that contributes to the regulation of temperature on the surface of the planet. It is believed that CH4 is responsible for 17% of global warming [7], taking into account the indirect chemical reactions of this gas with aerosols. The Global Warming Potential (GWP) of CH4 is estimated to be 25 times higher than the GWP of CO2 [8, 9] on a 100-year horizon.
The interest in estimates of CH4 emission in tropical forests has grown in recent years, particularly in wetlands such as the Amazon basin [10, 11, 12, 13, 14, 15] and Pantanal [16, 17, 18]. This is due to the fact that the largest natural sources of CH4 are wetlands [19], contributing with 177–284 Tg CH4 per year [7]. Humid areas are the largest and most uncertain sources of CH4 to the atmosphere [20]. Remote sensing techniques employing visible, infrared and microwave observations offer varying degrees of success in providing quantitative estimates of wetlands and inundation extent and monitoring natural and anthropogenic variations in these environments [21]. Another factor that may contribute to this uncertainty is the interannual variability of the water column associated to lakes and rivers, which directly influence the wetlands linked to them.
Wetlands have high C sequestration and store capacity, which justify the growing interest in studying the production and consumption of this gas in these ecosystems. The C sequestration refers to the removal of CO2 from the atmosphere, transfer, and accumulation of that gas in the flooded areas as soil organic matter. That is, the sequestration of C in wetlands is related to the photosynthetic removal of CO2 by producing organisms and its conversion into cellulose and other forms of C, and subsequently the transformation of waste into soil organic matter [22]. This ability to act on the C cycling, in addition to all other ecosystem services performed by those environments, makes them critical components in understanding local, regional, and global C stocks, capable of influencing the balance of CO2, CH4, and other GHG.
Floodplains are defined as environments that are seasonally flooded or saturated due to rising groundwater or surface water and remain like that for a certain period of the year or throughout the year [3]. According to Junk et al. [3], flooding of plains along rivers tends to occur as a single annual flood pulse that lasts months. In these plains, flooding can also lead to an increase in allochthonous inputs of C, making them essential to the food web and interesting to the scientific community.
The CH4 is produced mainly by microorganisms belonging to the domain
Part of the current understanding of the dynamics of CH4 in wetlands is based on the premise that most of the oxidation of CH4 occurs under aerobic conditions. However, recent studies indicate the action of several other electron acceptors (alternative to sulfate under aerobic conditions) in the anaerobic oxidation of CH4, including nitrate, nitrite, iron, and manganese [5, 26, 27, 28, 29, 30, 31, 32]. Studies also point to humic substances acting as a terminal acceptor for electrons in tropical flood areas [33]. In previous studies [32, 34], when attempting to justify the predominance of academic papers addressing the oxidation of CH4 exclusively by aerobic means, taking into account the fact that sulfate has been, for a long time, the only electron acceptor involved in the oxidation of CH4 in anoxic environments, the concentration of sulfate is generally too low in freshwater environments to play a role in the anaerobic oxidation of CH4. The contribution of anaerobic oxidation of CH4 to the methanotrophic processes is not fully elucidated, but the increasing number of papers validating the information shows that this mechanism seems to be more common than previously thought.
In turn, methanogenesis occurs when energetically favorable electron acceptors such as oxygen, nitrate, sulfate, and iron are absent or have been depleted [35]. In the absence of oxygen, the complete decomposition of complex organic compounds requires syntrophic system interactions in individual steps in the global process [36]. A sequential action involves hydrolysis, acidogenesis, acetogenesis, and methanogenesis steps [37]. Therefore, the many microbial guilds involved in those processes include hydrolytic, syntrophic fermentative, acetogenic, and methanogenic microorganisms.
Bacteria and fungi are responsible for breaking down complex molecules during hydrolysis, such as polysaccharides, proteins, and their forming units (amino acids, fatty acids, and alcohols) [38]. In the acidogenesis stage, fermentative microorganisms convert simple substrates into volatile fatty acids (VFA) (e.g., acetate, propionate, and butyrate), alcohols (e.g., ethanol and butanol), H2, and CO2 [39]. In acetogenesis, the VFA and alcohols produced, such as propionate, butyrate, and ethanol, are converted into acetate, H2, and CO2 by acetogenic bacteria [39]. Finally, methanogens convert acetate, H2/CO2, formate, and methylated compounds into CH4.
The amount of CH4 emitted from an ecosystem is the result of the balance between the production of CH4 (methanogenesis) and the consumption (oxidation) of this gas (methanotrophy). Therefore, the emission of CH4 into the atmosphere is determined by activity of methanogenic and methanotrophic microorganisms.
Methanogenic
The methanogenic microorganisms belong to the
The metabolism of methanogenic
Taking into consideration the production pathway (Figure 1), hydrogenotrophic methanogenic microorganisms are known as H2 oxidant, formate, or some simple alcohols and reduce CO2 to CH4 [43]. Most of the described methanogenic microorganisms are hydrogenotrophic. Acetoclastic methanogens divide acetate to form CH4 and CO2. They are found in habitats where hydrogenotrophic methanogenic microorganisms reduce H2 levels sufficiently to create the necessary conditions for the formation of high levels of acetate. Methylotrophic methanogenic microorganisms are common in sulfate-rich marine and hypersaline sediments, in which they use methylated compounds such as trimethylamine, dimethyl sulfate, and methanol [44]. In contrast, in sediments from freshwater environments, it is believed so far that methylotrophic methanogenesis is of little importance, although this is not what recent unpublished results have revealed for the floodable areas of the Amazon. However, the same reasoning used for anaerobic methanotrophy may be occurring in this case.
Conceptual illustration of CH4 production and consumption prior to atmospheric release in wetland ecosystems. Microorganisms degrade complex organic material in anoxic system by a multistep process, leading to CO2 and CH4 as end products. Adapted from [
Methyl compounds, especially methanol, may play an underestimated role as contributors to the production of CH4 in wetlands [44]. Although the use of methanol in the presence of hydrogen has been observed among methanogenic
Meyer et al. [46] used a metagenomic approach to assess the relative abundance of genes involved in cycling CH4 in forest and pasture soils in Western Amazon and they revealed that genes involved in methanogenesis from methylated compounds were significantly more abundant in the pasture. Soil methylotrophs call attention to the central role of these organisms in global methanol conversions, which mainly originate from plants [47] released from both living and decomposing plant material [48]. Soil microbiota is an essential component of plant decomposition and formation of organic matter. Thus, the understanding about these communities, as well as the one regarding decomposed material, is essential to elucidate the dynamics of these environments.
The literature mentions that in tropical alluvial plains the predominant microbial pathways in methane production are acetoclastic and hydrogenotrophic [49, 50]. However, Alves [51], when evaluating the enrichment of primary and secondary forest and pasture samples in the Amazon, indicated a higher production of CH4 by acetoclastic and methylotrophic pathways.
In flooded areas, known to have high methanogenic rates, methanotrophs are responsible for catalyzing the oxidation of CH4 at the aerobic-anaerobic interfaces. Methanotrophic bacteria are able to use CH4 as their sole source of C [52] and can be divided into four groups: Gammaproteobacteria (often referred as Type I or Type X); Alphaproteobacteria (formerly known as Type II); Verrucomicrobia; and NC10 phylum members [53].
Methanotrophic activity is only viable because of an enzyme known as monooxygenase methane (MMO), which acts in two distinct forms: particulate (pMMO), within an intracellular membrane, or soluble (sMMO), in the cytoplasm. Both convert CH4 into the readily assimilated product, methanol [54].
The oxidations of CH4 have proven to be an important sink for this gas produced by sediments in the Amazon, reducing the amount of CH4 that reaches the atmosphere [8, 55].
The diversity of CH4 metabolizers or metabolizing organisms tends to increase in the near future due to additional findings in surveys using a metagenomic approach and other increasingly robust approaches to the study of microbial diversity. This can be the currently ambiguous evolutionary history of this important metabolic function [23].
Alluvial plains are among the most dynamic ecosystems, consisting of a mosaic of habitats with high spatial–temporal turnover rates [4, 56]. The complex interaction between the topography of the floodplain and the variation in river flow and sediment transport maintains a distinct gradient of lateral hydrological connectivity, which facilitates the coexistence of numerous aquatic, amphibian, and terrestrial species [4].
These sites exhibit highly heterogeneous stratigraphy produced by active river meanders and sediment deposition; dominant coarse-grained materials are interspersed with finer sediments, and organic matter is deposited, leading to distinct zones of oxic or anoxic conditions within the subsurface [57]. Regional variations in fresh water CH4 emissions are important factors that should be considered to ensure reliable global estimates. The C stocks, as well as the different classes of organic matter, still need to be elucidated in order to decrease our limitations in building C cycling models in those environments. Hydrological variations are responsible for determining the intensity and duration of aerobic conditions. Changes in these conditions can increase or decrease the rates of decomposition of organic matter [58].
The Colombian wetlands were evaluated and showed that the studied ecosystems are valuable C sinks, and hydrogeomorphology acts as an important factor for the storage of C in these ecosystems [59]. Dalmagro et al. [18], when evaluating the largest tropical floodplain area in the world, Pantanal, revealed that they are potentially large C sinks and that the C balance was driven by the seasonal dynamics of precipitation and surface flooding that affected the anaerobic and aerobic phases of the soil. The assessment of the behavior of a freshwater flood area with a usual average flood period of 6 months per year, located in a park in the USA, showed that the environment become a source of CO2 when it went through a prolonged flood period (17 months). Such situations may occur more frequently in the future, as an intensification of rainfall is expected. Moreover, evidence suggests that the magnitude of wet and dry cycles can have significant effects on GHG emission [60].
Alluvial plains are environments adapted to variations in water level, and it seems that microbial communities can be adapted to these fluctuations and remain in a state of latency until the next flood. Hernández et al. [61] demonstrated that in Amazon forest soils the propensity to produce CH4 (at the laboratory) was best observed in relation to the duration of the lag phase. Soils that were never flooded (dry forest) presented this phase for a longer time than sites that were permanently flooded. In a laboratory study that imposed different levels of flood frequency, differences in composition have also been observed, but an increase in diversity under conditions of higher water saturation has been reported [62].
Alluvial plains are among the most threatened ecosystems in the world because of anthropogenic activities, especially in developing countries, where high demand from agricultural areas drives deforestation. An additional threat to those ecosystems is the increase in terrestrial temperatures due to global warming, generating a cycle of change in water regime that may consequently alter the storage capacity of C in wetland ecosystems [63].
Current projections suggest that rates of GHG emission from floodplains will increase as global average temperatures continue to rise, and this is of particular importance in temperate and tropical systems. The metabolism of CH4 in flooded areas is strongly influenced by environmental factors that have both spatial and temporal variability. The production and consumption of GHG are partially regulated by microbial processes, which are influenced by soil moisture and temperature [64]. In soils, the microbial production rate of CH4 generally shows an exponential relationship with air temperature, with the peak rate corresponding to temperatures of 25°C (77 F) to 30°C (86 F) [65].
Wetlands are likely to become the main net sources of C under the effect of warming climatic conditions in decades [64]. Sanches et al. [66] determined the crucial factors related to the emission of CH4 in lakes, on a large scale, observing emission patterns in different climate zones. The climate zones with the highest average air temperatures showed the highest emission rates.
Climate change in tropical wetlands is expected to cause an increase in temperature and a change in precipitation patterns, increasing the duration of the dry season, but also increasing the intensity of precipitation events. Given these predictions, the current and future balance of seasonally flooded tropical areas is still uncertain. A study conducted from 2014 to 2016 in the largest continuous wetland area in the world, the Brazilian Pantanal, demonstrated the response of CH4 and CO2 to the hydrological dynamics of this ecosystem [18]. Measurements revealed that CH4 emission increased rapidly as soon as anaerobic conditions were established, with the highest CH4 flow values having always been observed when soil redox potential values were less than −100 mV. In summary, the data indicated that the seasonally flooded rainforests of Pantanal are potentially large C sinks, but significant sources of CH4 when anaerobic conditions dominate the soil (flood period). It is worth mentioning that the carried out measurements contemplate emission from tree trunks, soil flows, boiling, and diffusion from the water surface, since the methodology used was a 20 m (65.6 feet) high research tower, together with environmental sensors. A recent research suggests that CH4 emission from tree branches are the dominant source of regional CH4 emission to flooded tropical forest environments [11].
Understanding the level of sensitivity of flooding areas as a response to climate change also requires efforts to be better achieved. This demands more knowledge at all levels, ranging from single-cell ecophysiology to
It is a challenge for researchers to design experiments and adopt methods that can detect C cycling in alluvial plains. One of the approaches used is the characterization of microbial communities in space and time from the sequencing of DNA or RNA and the construction of correlation matrices of relative abundances of microbial taxa or functional groups with environmental variables [68]. Regardless of the study method, the characterization needs to reflect the correct scale for the issue and should contain enough replicates to provide meaningful data [69].
Metagenomic studies indicate high functional redundancy in flooding areas. Although we know that microbial communities are diverse, DNA-based methods can artificially inflate functional redundancy estimates [69]. This fact is due to the DNA-based approach not being able to distinguish between dormant and active cells [70].
Different responses have been found when assessing the effect of increased salinity on the composition of the total (DNA) and active (RNA) microbial community in an anaerobic reactor [71]. The concentration and exposure time most strongly affected the microbial community, and especially the
The evaluation of the survival conditions of
Stable isotopes have long been used as a tool to investigate environmental processes and their relationships with microorganisms, which can be established through metabolic pathways [73]. The merit of C isotopes lies in their relatively slow exchange rate for many minerals containing C and in relatively large fractionations, even at high temperatures. These two properties make C isotopes an excellent recorder of geological processes and allow a better understanding of C sources and related-volatile flows in geological time scales [74]. We are undergoing a reformulation of isotopic approaches based on the increase in genomic and transcriptomic databases, the latest technologies with improved instrumental and mass spectrometric data acquisition, processing, and evaluation [73]. For Coyotzi et al. [75], the incorporation of stable isotopes into the microbial biomass allows the recovery of labeled nucleic acids from active microorganisms. The combination of stable isotopes with metagenomics provides access to the genomes of microorganisms involved in metabolic processes of interest.
The CH4 fluxes in terrestrial and aquatic environments have been evaluated in several ways, but in general, the monitoring of gas accumulation in flow chambers has been the predominant methodology to date. This methodology is conceptually simple and does not require expensive field equipment, but is laborious, based mainly on manual sampling [17].
To improve the prediction of climate models, it is important to understand the mechanisms by which microorganisms regulate the flow of terrestrial GHG. This involves considering the complex interactions that occur between microorganisms and other biotic and abiotic factors in the environment. The potential to mitigate climate change by reducing GHG emission through the management of terrestrial microbial processes is a perspective of high importance for the future [18].
Despite this importance, however, tropical flood areas are poorly represented in global models to predict global CH4 emission. A first step in the development of a process-based model of CH4 emission from tropical flood areas for global applications was documented in 2014. To this end, the LPX-Bern Dynamic Global Vegetation Model (LPX) was slightly modified to represent the hydrology of the floodplain, vegetation, and associated CH4 emission. The extent of tropical floodplains was prescribed using the production of the spatially explicit PCR-GLOBWB hydrology model. Several variables were introduced to this model, such as vegetation, ground cover (through remote sensing), not to mention that simulated CH4 flow densities were evaluated against field observations and regional flow inventories. However, soil microbiota was not considered as a component in the modeling. Simulated CH4 emissions at the Amazon basin scale were compared to simulations of previously performed models. Thus, it was found that this LPX model reproduces the average magnitude of the net flow densities of CH4 observed for the Amazon basin. However, the model does not reproduce the temporal and spatial variability between sampling sites, considering that site information is too limited to attest or refute some resources of the model. At the Amazon basin scale, the results obtained with the promotion of this model highlighted the great uncertainty in the magnitude of CH4 emission from floodable areas.
The sensitivity analysis provided clarification on the main drivers of CH4 emission from the floodplain and their associated uncertainties. Due to an intrinsic limitation of the LPX to consider seasonality in floodplain extension, the model failed to reproduce the total dynamics of CH4 emission, raising several scientific questions. Although this model includes more specific mechanisms for tropical floodplains, it was not possible to reduce the uncertainty in the magnitude of CH4 emission from the Amazon basin, thus justifying the need for further research to restrict CH4 emission and their temporal variability [15].
In the same year, Potter et al. [76] developed a new model that sought to seasonally estimate the carbon dynamics and CH4 emission of floodable ecosystems in the Amazon. The Amazon wetland simulation model took into account three main components: (a) details of the type of vegetation in the wetlands and changes in the level of water, temperature, and dissolved oxygen; (b) primary production, mass accumulation, and decay of the litter layer in soils and sediments; and (c) routes for production and transport of CH4 through the water column to the atmosphere.
The presented model is based on the input of the following data for simulations in a given flooded environment in the Amazon: latitude and longitude; vegetation types such as area cover fractions; daily surface temperature; solar irradiance flux; wind speed; precipitation; daily water depth; biomass production values for floating macrophytes; and satellite vegetation index data for flooded forest ecosystems. In order to improve the generality and use of this model, the incorporation of mechanical simulations of vertical mixing, horizontal exchanges, and various biogeochemical processes is necessary. In addition, the microbiota component is not directly reported.
In 2016 [77], when evaluating the atmospheric concentrations of CH4 in the Amazon basin in 2010 and 2011, besides a 3D atmospheric chemical transport model (TOMCAT), two emission models in wetlands have been used [78, 79] to reduce the uncertainty about CH4 emission. The first set of wetland and rice paddy emission derived from the Bloom et al. method [79]. The method uses a satellite to evaluate the carbon variation available for methanogenesis, which leads to a more accurate representation of the timing of CH4 emission. However, satellite data cannot distinguish between microbial CH4 emission from natural wetlands and anthropogenic emission from rice cultivation. The second model [the Joint UK Land Environment Simulator (JULES), version 3.4.1] [78] simulates the Earth’s land surface in terms of carbon, water, and energy variations and includes a methane flux in wetlands as a component, based on Gedney et al. [80]. The flow of CH4 is dependent on the available carbon substrate, the temperature, and the fraction considered wet. The estimates used through the two wetlands emission models are based on processes and showed similar behaviors when the atmospheric model is compared to observations, regardless of which model was used [77].
In the same year (2016), another research on CH4 modeling was carried out, bringing to the fore the discussion of how beneficial the improvements in CH4 models would be for terrestrial system models and for the additional simulation of climate-carbon cycle feedbacks. Over the past four decades, several empirical models have been developed to quantify the magnitude, investigate spatial and temporal variations, and understand the underlying mechanisms and environmental controls of CH4 (CH4 flows in terrestrial ecosystems). These CH4 models are also used for the integration of multiple-scale CH4 data, such as laboratory-based incubation and molecular analysis, field observational experiments, remote sensing, and aircraft-based measurements in various terrestrial ecosystems. The authors noted that there are large discrepancies between models in terms of representation of CH4 processes and their environmental controls, and significant data, such as model incompatibilities, are partially attributed to different representations of landscape characterization and flood dynamics.
However, it should be noted that CH4 models should represent more explicitly the mechanisms underlying the exchange of Earth-atmosphere CH4, with emphasis on the improvement and validation of individual CH4 processes over depth and horizontal space, and models capable of simulating CH4 emissions at highly heterogeneous spatial and temporal scales, particularly in hotspots, should be developed; besides that, efforts should be made to develop benchmarking models (a modeling based on comparative analysis) that can be easily used for improvement, evaluation, and integration with data from molecular to global scales [81].
Widely applicable and robust prediction models should be developed from large data sets generated through collaboration with scientists around the world. To achieve high predictive accuracy, these data sets should cover a wide variety of information and variables at the most different scales of tropical floodplains within regions and globally.
This chapter synthesizes the main progresses in scientific research applied to understanding the dynamics of CH4 in tropical floodplains. Here, we focused on an integrative approach to the main aspects of the C cycle, describing methods based on observations on the Earth’s surface. However, a better understanding of the methylotrophic methanogenesis and anaerobic oxidation of CH4 still needs to be clarified for these environments. The next generation of models of CH4 emission should take into account seasonal water level fluctuation and the methanogenic and methanotrophic activity associated with it. This bold goal can only be achieved using a multianalytical approach based on a synergy of models, statistical methods for data integration, and scientific cooperation. This effort can help to create a unique design, in which not only the biosphere and the feedback of hydrological modeling but also the soil microbiota will be considered in the regional cycle of C.
This study was supported by a grant from Fundação de Amparo à Pesquisa do Estado de São Paulo (Foundation for Research Support of the State of São Paulo) (FAPESP 2016/16687-3). DJB and MSF were supported by the CAPES Financial Code 001. MSB was supported by FAPESP 2017/06415-9 and FAPESP 2018/02277-3. AAN was supported by FAPESP 2017/03575–5.
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Since no superfluid has exact zero viscosity, we analyze the consequences of SQS’s viscosity on light propagation, and we show that a static Universe could be possible, by solving a modified Navier-Stokes equation. Indeed, Hubble’s law may actually refer to tired light, though described as energy loss due to SQS’s nonzero viscosity instead of Compton scattering, bypassing known historical problems concerning tired light. We see that SQS’s viscosity may also account for the Pioneer anomaly. Our evaluation gives a magnitude of the anomalous acceleration aP = −HΛc = −8.785°10−10 ms−2. Here, HΛ is the Hubble parameter loaded by the cosmological constant Λ. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. 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Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. 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