These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\n
This collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\n
To celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\n
Initially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\n
This collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\n
To celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"969",leadTitle:null,fullTitle:"Lung Diseases - Selected State of the Art Reviews",title:"Lung Diseases",subtitle:"Selected State of the Art Reviews",reviewType:"peer-reviewed",abstract:"The developments in molecular medicine are transforming respiratory medicine. Leading clinicians and scientists in the world have brought their knowledge and experience in their contributions to this book. Clinicians and researchers will learn about the most recent advances in a variety of lung diseases that will better enable them to understand respiratory disorders. This treatise presents state of the art essays on airways disease, neoplastic diseases, and pediatric respiratory conditions. Additionally, aspects of immune regulation, respiratory infections, acute lung injury/ARDS, pulmonary edema, functional evaluation in respiratory disorders, and a variety of other conditions are also discussed. 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He applied for two patents at the national level.",coeditorOneBiosketch:"A renowned researcher in the field of Agricultural Engineering with 14 years of academic experience at Bahauddin Zakariya University. Winner of various prestigious fellowships, awards, and research grants. Published 250+ articles along with several books and chapters. Guest editor of seven ISI-SCI journals for publishers like SAGE, MDPI, and Frontiers.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"338219",title:"Dr.",name:"Fiaz",middleName:null,surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/338219/images/system/338219.png",biography:"Dr. Fiaz Ahmad is an assistant professor and lecturer at the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan. He obtained his Ph.D. in Agricultural Bioenvironmental and Energy Engineering from Nanjing Agriculture University, China, in 2015, and completed his postdoctorate in Agricultural Engineering from Jiangsu University, Zhenjiang, China, in 2020. He was awarded a fellowship from the Higher Education Commission of Pakistan for Ph.D. studies and from the Chinese Government for post-doctoral studies. He earned a BSc and MSc (Hons) in Agricultural Engineering from the University of Agriculture, Faisalabad, Pakistan, in 2004 and 2007, respectively. He is the author of more than fifty journal and conference articles. He has supervised six master’s students to date, and is currently supervising six master and two doctoral students. Dr. Ahmad has completed three research projects with his research interest focusing on the design of agricultural machinery, agricultural waste management, artificial intelligence (AI), and agricultural bioenvironment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:{id:"199381",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sultan",slug:"muhammad-sultan",fullName:"Muhammad Sultan",profilePictureURL:"https://mts.intechopen.com/storage/users/199381/images/system/199381.png",biography:"Muhammad Sultan is an Assistant Professor at the Department of Agricultural\r\nEngineering, Bahauddin Zakariya University, Multan (Pakistan). He completed his Ph.D.\r\nand Postdoc from Kyushu University (Japan) in the field of Energy & Environmental\r\nEngineering. He was an awardee of MEXT and JASSO fellowships (from the Japanese\r\nGovernment) during Ph.D. and Postdoc studies, respectively. He also did a Postdoc as\r\na Canadian Queen Elizabeth Advance Scholar at Simon Fraser University (Canada) in\r\nthe field of Mechatronic Systems Engineering. He worked for Kyushu University\r\nInternational Institute for Carbon-Neutral Energy Research (WPI-I2CNER) for two years.\r\nCurrently, he is working on 4 research projects funded by the Higher Education\r\nCommission (HEC) of Pakistan. He has completed six projects in past in the field of\r\nagricultural engineering. He has supervised 10+ M.Eng. and Ph.D. thesis and 10+\r\nstudents are currently working under his supervision. He has published 120+ journal\r\narticles, 100+ conference articles, 13 book chapters, and 6 books. 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1. Introduction
The increasing use of Internet has dramatically contributed to the growing number of threats that inhabit within it. Seeking for a better protection, Computer Security and, specifically, Network Intrusion Detection Systems (NIDS) have risen to become a topic of research and concern in order to fight these threats.
More accurately, a NIDS is a type of computer software that is able to distinguish legitimate network users from malicious ones. Moreover, due to the rising complexity and volume of the attacks, NIDS are performed in an automated manner, so the NIDS software monitors system usage to identify behaviour breaking the security policy. Generally, NIDS are categorised based in their scope: misuse network detectors and anomaly detectors. On the one hand, misuse detection systems deal with menaces already known in beforehand. Basically, these systems manage a comprehensive attack base and their work consists of invigilating at all incoming traffic to detect any sequence that appears in that knowledge base.
On the other hand, anomaly detection systems are more ambitious and try to discover new unknown threats (the so-called zero-day attacks). To this extent, these systems model benign or legitimate system usage in order to thereafter obtain a certainty measure of potential deviations from that normal profile. Each deviation that is found significant enough will be considered anomalous and notified to a human operator. Research in network anomaly detection has applied several well-known Artificial Intelligence paradigms such as finite automata (Vigna et al., 2000), neural networks (Mukkamala et al., 2005), genetic algorithms (Kim et al., 2005), fuzzy logic (Chavan et al., 2004), support-vector machines (Mukkamala et al., 2005) or diverse data-mining-based approaches (Lazarevic et al., 2003).
Actually, these solutions, both misuse and anomaly, perform better or worse against a network attack. Misuse detection systems are overwhelmed since they cannot face menaces that have not been previously described in their rule base but they overcome very fast the ones that have. Unfortunately, anomaly detection itself may not be considered as the the perfect solution, as well. In this way, it is much less exact than misuse detectors with well-known attacks and, despite they do find zero-day threads, sometimes they also produce false positives (i.e. select as a menace what is perfectly right). Summarizing, each approach is clearly surpassed when it comes to the other’s area of expertise and the goal is, thus, to find the way to integrate both system’s benefits while reducing their weaknesses.
In this way, Bayesian networks (Pearl & Russell, 2000) represent the sort of tool that can help us to achieve this integration. Specifically, they are probabilistic models very helpful when facing problems that require predicting the outcome of a system consisting of a high number of interrelated variables. After a training period, the Bayesian network learns the behaviour of the model and, thereafter it is able to foresee its outcome. In this way, successful applications of Bayesian networks include for instance email classification for spam detection (Yang et al., 2006), failure detection in industrial production lines (Masruroh & Poh, 2007) (Liu & Li, 2007), weather forecasting (Abramson et al., 1996) (Cofiño et al., 2002), intrusion detection over IP networks (Krügel et al., 2003) (Faour et al., 2006) or reconstruction of traffic accidents (Davis & Pei, 2003) (Davis, 2006). In all cases, the respective target problem is modelled as a constellation of interconnected variables whose output is always the result of the prediction (e.g. spam found, failure detected, intrusion noticed and so on). Therefore, we can model a NIDS as a constellation of variables controlling the type of the traffic, information on packet headers, packet payload or their temporal relationships (i.e. to check whether they form a coordinated attack). If we connect this representation to an attack variable, we will be able, after a proper training, to predict when do incoming packets represent a menace to the system.
Given this background, we present ESIDE-Depian (Intelligent Security Environment for Detection and Prevention of Network Intrusions), the first inherently unified misuse and anomaly detector. Besides, we focus on the integration of anomaly and misuse and show how this goal can be achieved by using a Bayesian network. In addition, we test this integration with real network attacks and show ESIDE-Depian’s efficiency both as misuse and as anomaly detection.
The remainder of the chapter is organised as follows. follows. Section 2 illustrates the differences between misuse and anomaly detections systems. Section 3 details the concept of a Bayesian network and describes the used in ESIDE-Depian. Section 4 describes how ESIDE-Depian integrates misuse and anomaly prevention. Section 5 presents the experiments to evaluate this integration and discuses their results. Section 6 concentrates on the problems appeared and the solution designed to solve them. Section 7 discusses related work and, finally, section 8 concludes and outlines the avenues of future work.
2. Misuse versus Anomaly Detection
Currently, misuse detection is the most extended approach for intrusion prevention, mainly due to its efficiency and easy administration (Bringas et al., 2009). Its philosophy is quite simple: based on a rule base that models a high number of network attacks, the system compares incoming traffic with the registered patterns to identify any of these attacks. Hence, it does not produce any false positive (since it always finds exactly what is registered) but it cannot detect any new threat. Further, any slightly-modified attack will pass unnoticed. Finally, the knowledge base itself poses one of the biggest problems to misuse detection: as it grows, the time to search on it increases as well and, finally, it may require too long to be used on real-time.
Anomaly detection systems, on the contrary, start not from malicious but from legitimate behaviour in order to model what it is allowed to do. Any deviation from this conduct will be seen as a potential menace. Unfortunately, this methodology is a two-sided sword since, though it allows to discover new unknown risks, it also produces false positives (i.e. packets or situations marked as attack when they are not). In fact, minimising false positives is one of the pending challenges of this approach (Kruegel, 2002). Moreover, misuse detection presents a constant throughput since its knowledge base does not grow uncontrollably but gets adapted to new situations or behaviours. Again, an advantage is also source of problems because it is theoretically possible to make use of this continuous learning to little by little modify the knowledge so it ends seeing attacks as proper traffic (in NIDS jargon, this phenomenon is known as session creeping). In other words, its knowledge tends to be unstable. Finally, anomaly detection, unlike misuse, demands high maintenance efforts (and costs). In summary, both alternatives present notable disadvantages that demand a new approach for network intrusion prevention.
3. Bayesian-network-based intrusion detection
3.1 Background
Reverend Thomas Bayes pioneered with his work the research on cause-consequence relationships. The most important fruit of that investigation, known as the “Bayes’ theorem” (Bayes, 1763) in his honour, is the basis of the so-called Bayesian inference, a statistical inference method that allows, upon a number of observations, to obtain or update (if the system is already working) the probability that a hypothesis may be true. In this way, Bayes’ theorem adjusts the probabilities as new informations on evidences appear.
According to its classical formulation, given two events A and B, the conditional probability P(A|B) that A occurs if B occurs can be obtained if we know the probability that A occurs, P(A), the probability that B occurs, P(B), and the conditional probability of B given A, P(B|A) (as shown in equation 1):
PA|B=PB|A⋅PAPBE1
More accurately, Bayesian Networks (Pearl & Russell, 2000) are defined as graphical probabilistic models for multivariate analysis. Specifically, they are directed acyclic graphs that have an associated probability distribution function (Castillo et al., 1996). Nodes within the directed graph represent problem variables (they can be either a premise or a conclusion) and the edges represent conditional dependencies between such variables. Moreover, the probability function illustrates the strength of these relationships in the graph (Castillo et al., 1996) (Figure 1).
Fig. 1.
Example of a Bayesian Network.
Formally, let a Bayesian Network B be defined as a pair, B = (D, P), where D is a directed acyclic graph; P = {p(x1 |Ψ2), …, p(xn |Ψn)} is the set composed of n conditional probability functions (one for each variable); and Ψi is the set of parent nodes of the node Xi in D. The set P is defined as the joint probability density function (Castillo et al., 1996) (equation 2)
Px=∏i=1npxi|ΨiE2
The most important capability of Bayesian Networks is their ability to determine the probability that a certain hypothesis is true (e.g., the probability of an e-mail to be spam or legitimate) given a historical dataset.
3.2 Bayesian Network Obtaining Process
The obtaining of the knowledge model in an automated manner can be achieved in an unsupervised or supervised way.
Typically, unsupervised learning approaches don not take into consideration expert knowledge about well-known attacks. They achieve their own decisions based on several mathematical representations of distance between observations from the target system, revealing themselves as ideal for performing Anomaly Detection.
On the other hand, supervised learning models do use expert knowledge in their making of decisions, in the line of Misuse Detection paradigm, but usually present high-cost administrative requirements. Therefore, both approaches present important advantages and several shortcomings. Being both ESIDE-Depian, it is necessary to set a balanced solution that enables to manage in an uniform way both kinds of knowledge.
Therefore, ESIDE-Depian uses not only Snort information gathering capabilities, but also Snort’s decision-based labelling of network traffic. Thereby, the learning processes inside ESIDE-Depian can be considered as automatically-supervised Bayesian learning, divided into the following phases. Please note that this sequence only applies for the standard generation process followed by the Packet Header Parameter Analysis experts (see Figure 2).
Fig. 2.
ESIDE-Depian general architecture integrating misuse and anomaly detection.
We have divided the network traffic according to its type (TCP-IP, UDP-IP and ICMP-IP) and created three Bayesian networks (experts) to analyse their respective packet headers (which is an strategy already proven successful in this area (Alípio et al., 2003)). Moreover, in order to cover all possible kind of menaces, we also have to take into account the payload (i.e. body) of the packet and the potential temporal dependencies between packets. Therefore, we have added 2 further experts, the protocol payload and the connection tracking one, respectively. In each case, the Bayesian network is composed of several variables depending on the protocol and the expert; the value to induce is always the probability that the analysed packet is part of an attack.
Moreover, the creation and setting-up of each Bayesian network comprises the following phases:
Traffic sample obtaining. First we need to stablish the information source in order to gather the sample. This set usually includes normal traffic (typically gathered from the network by sniffing, arp poisoning or so), as well as malicious traffic generated by the well-known arsenal of hacking tools (e.g. MetaSploit1).
Structural Learning.
The next step is devoted to define the operational model ESIDE-Depian should work within. With this goal in mind, we have to provide logical support for knowledge extracted from network traffic information. Packet parameters need to be related into a Bayesian structure of nodes and edges, in order to ease the later conclusion inference over this mentioned structure.
In particular, the PC-Algorithm (Spirtes et al., 2001) is used here to achieve the structure of causal and/or correlative relationships among given variables from data. In other words, the PC-Algorithm uses the traffic sample data to define the Bayesian model, representing the whole set of dependence and independence relationships among detection parameters.
Parametric Learning. The knowledge model fixed so far is a qualitative one. Therefore, the following step is to apply parametric learning in order to obtain the quantitative model representing the strength of the collection of previously learned relationships, before the exploitation phase began.
Specifically, ESIDE-Depian implements maximum likelihood estimate (Kjærulff & Madsen, 2008) to achieve this goal. This method completes the Bayesian model obtained in the previous step by defining the quantitative description of the set of edges between parameters. This is, structural learning finds the structure of probability distribution functions among detection parameters, and parametric learning fills this structure with proper conditional probability values.
Bayesian Inference. Next, every packet capture from the target communication infrastructure needs one value for the posterior probability of a badness variable, (i.e. the Snort 2 label), given the set of observable packet detection parameters.
Hence, we need an inference engine based on Bayesian evidence propagation. More accurately, we use the Lauritzen and Spiegelhalter method for conclusion inference over junction trees, provided it is slightly more efficient than any other in terms of response time (Castillo et al., 1996). Thereby, already working in real time, incoming packets are analysed by this method (with the basis of observable detection parameters obtained from each network packet) to define the later probability of the attack variable.
The continuous probability value produced here represents the certainty that an evidence is good or bad. Generally, a threshold-based alarm mechanism can be added in order to get a balance between false positive and negative rates, depending on the context.
Adaptation. Usually, the system operation does not keep a static on-going way, but usually presents more or less important deviations as a result of service installation or reconfiguration, deployment of new equipment, and so on.
In order to keep the knowledge representation model updated with potential variations in the normal behaviour of the target system, ESIDE-Depian uses the general sequential/ incremental maximum likelihood estimates (Castillo et al., 1996) (in a continuous or periodical way) in order to achieve continuous adaptation of the model to potential changes in the normal behaviour of traffic.
3.3 Connection Tracking and Payload Analysis Bayesian Experts Knowledge Model Generation
The Connection Tracking expert attends to potential temporal influence among network events within TCP-based protocols (Estevez-Tapiador et al., 2003), and, therefore, it requires an structure that allows to include the concept of time (predecessor, successor) in its model. Similarly, the Payload Analysis expert, devoted to packet payload analysis, needs to model state transitions among symbols and tokens in the payload (following the strategy proposed in (Kruegel & Vigna, 2003).
Generally, Markov models are used in such contexts due to their capability to represent problems based on stochastic state transitions. Nevertheless, the Bayesian concept is even more suited since it not only includes representation of time (in an inherent manner), but also provides generalization of the classical Markov models adding features for complex characterization of states.
Specifically, the Dynamic Bayesian Network (DBN) concept is commonly recognized as a superset of Hidden Markov Models (Ghahramani, 1998), and, among other capabilities, it can represent dependence and independence relationships between parameters within one common state (i.e. in the traditional static Bayesian style), and also within different chronological states.
Therefore, ESIDE-Depian implements a fixed twonode DBN structure to emulate the Markov-Chain Model (with at least the same representational power and also the possibility to be extended in the future with further features) because full-exploded use of Bayesian concepts can remove several restrictions of Markov-based designs. For instance, it is not necessary to establish the first-instance structural learning process used by the packet header analysis experts since the structure is clear in beforehand.
Moreover, according to (Estevez-Tapiador et al., 2003; Kruegel & Vigna, 2003), the introduction of an artificial parameter may ease this kind of analysis. Respectively, the Connection Tracking expert defines an artificial detection parameter, named TCP-h-flags (which is based on an arithmetical combination of TCP flags) and the Payload Analysis expert uses the symbol and token (thus, in fact, there are two Payload Analysis experts: one for token analysis and another for symbol analysis).
Finally, traffic behaviour (and so TCP flags temporal transition patterns) as well as payload protocol lexical and syntactical patterns may differ substantially depending on the sort of service provided from each specific equipment (i.e. from each different IP address and from each specific TCP destination port). To this end, ESIDE-Depian uses a multi-instance schema, with several Dynamic Bayesian Networks, one for each combination of TCP destination address and port. Afterwards, in the exploitation phase, Bayesian inference can be performed from real-time incoming network packets.
In this case, the a-priori fixed structure suggests the application of the expectation and maximization algorithm (Murphy, 2001), in order to calculate not the posterior probability of attack, but the probability which a single packet fits the learned model with.
3.4 Naive Bayesian Network of the Expert Modules
Having different Bayesian modules is a twofold strategy. On the one hand, the more specific expertise of each module allows them to deliver more accurate verdicts but, on the other hand, there must be a way to solve possible conflicting decisions. In other words, an unique measure must emerge from the diverse judgements.
To this end, ESIDE-Depian presents a two-tiered schema where the first layer comprises the expert modules (TCP-IP, UDP-IP, ICMP-IP, Connection Tracking and Protocol Payload) and the second layer includes only one class parameter: the most conservative response of the experts (in order to prioritize the absence of false negatives in front of false positives). Both layers form, in fact, a naive Bayesian network.
Such a Naive classifier (Castillo et al., 1996) has already been proposed in network intrusion detection, mostly for anomaly detection (Amor et al., 2004). This approach provides a good balance between representative power and performance, and also affords interesting flexibility capabilities which allow, for instance, ESIDE-Depian’s dynamical enabling and disabling of expert modules. Figure 3 details the individual knowledge models and how do they fit to conform the general one.
Fig. 3.
ESIDE-Depian Final Knowledge Representation Model.
4. Integration of Misuse and Anomaly Detection
The internal design of ESIDE-Depian is principally determined by its dual nature. Being both a misuse and anomaly detection system requires answering to sometimes clashing needs and demands. In other words, it must be able to simultaneously offer efficient response against both well-known and zero-day attacks. The Bayesian network, according to the ability to extrapolate its knowledge and apply it to not-previously seen cases, is the ideal tool for these zero-day attacks. Still, we have to integrate detection of already registered threads and provide an efficient methodology to update and to continuously adapt to changes. ESIDE-Depian achieves this objectives in two ways. First, it incorporates Snort to the training of the Bayesian network. Second, already in working-time, Snort’s opinion is passed to the experts so they can take this additional information into account.
4.1 Snort-driven Automated Learning
The obtaining of the knowledge model in an automated manner can be achieved in an unsupervised or supervised way. In the training phase, Snort provides information regarding the legitimacy or malice of the network packets. Specifically, Snort’s main decision about a packet is added to the set of detection parameters, receiving the name of attack variable. In this way, it is possible to obtain a complete sample of evidences, including, in the formal aspect of the sample, both protocol fields as well as Snort labelling information. Therefore, it combines knowledge about normal behaviour and also knowledge about well-known attacks, or, in other words, information necessary for misuse detection and for anomaly detection.
4.2 Snort-labelled Network Traffic
Initial designs of ESIDE-Depian considered including Snort’s opinion at the same level as experts’ verdict in the naive Bayesian network but experiments showed that it biased the result too much. Therefore, we chose an strategy similar to the one used in the Bayesian network training (described in the previous section). Hence, already in real time, every packet gets Snort’s opinion added as the badness variable mentioned before. In this way, experts know again the decision of Snort in beforehand and can act in consequence according to their knowledge model. Figure 2 illustrates how Snort is integrated within the different modules that conform ESIDE-Depian.
5. Evaluation and results
In order to asses the performance of ESIDE-Depian both as misuse and as anomaly detector, we have performed different kinds of experiments. Since Snort analyses only superficially the body of each packet, we have been forced to divide these tests into header-based and packet-body-based attacks in order to evaluate all of them more efficiently.
5.1 Header Parameter Analysis
Three are the Bayesian experts involved in this series of tests (though this does not mean that only one expert deals with the analysis; the naive Bayesian network considers all of them before obtaining the final verdict): TCP-IP, UDP-IP and ICMP-IP experts. The methodology applied intends to, first, demonstrate that the initial reference knowledge has been acquired, and second, that this reference knowledge has been superseded and exceed. In other words, we initially test the misuse detection capability and then, the anomaly detection ability.
The acquisition of the initial reference knowledge is performed already in the training phase. The BN is fed with a traffic sample basically based on the attack-detection rules battery provided by Snort. Therefore, the training acquaints the BN with either kind of traffic simultaneously, good and bad. Still, due to the disparity in the amount of packets belonging to one or another (see Table 1), traces containing attacks have to be fed several times (in the so-called presentation cycles) in order to let the BN learn to evaluate them properly. Table 1) summarises the results of testing the initial (Snort) reference knowledge acquisition. To this end, the BN was fed with a new sample traffic merging normal one extracted from a one hour capture at the University of Deusto and also malicious packets (crafted with the tool PackIt).
Traffic type
TCP
UDP
ICPM
Reference knowledge good/bad traffic ratio
699,560/42
5,130/11
1,432/95
Presentation cycles required
2943
2
2
Snort’s hits
38
0
450
Analysed packets
100,000
10,000
5,000
Attacks detected by Snort
5
1
600
Attacks detected by ESIDE-Depian
5 (100%)
1 (100%)
600 (100%)
Table 1.
Misuse Detection Tests Analysing Packets Headers.
ESIDE-Depian shows the same performance as Snort in these tree different traffic sorts. The high number of presentation cycles required by the TCPIP expert to grasp the initial reference knowledge is due to the very high good/bad traffic ratio, much lower in the cases of UDP and ICMP. Therefore, we can conclude that gaining the reference knowledge was completed successfully. Regarding going beyond this reference knowledge (i.e. the ability of ESIDE-Depian to find zero-day attacks) we have created artificial anomalies along to the proposal of Lee et al. (2001). In this way, table 2 shows some of the TCP-IP packets that we inserted in the traffic (crafted to this end again with PackIt).
Examples of Anomalies
packit -nnn -s 10.12.206.2
-d 10.10.10.100 -F SFP -D 1023
packit -nnn -s 10.12.206.2
-d 10.10.10.100 -F A -q 1958810375
packit -nnn -s 10.12.206.2
-d 10.10.10.100 -F SAF
Anomaly detection results
Potential false positives (anomalous packets)
15
Anomaly detection rate
100%
Table 2.
Anomaly Detection Tests for TCP-IP Traffic.
Snort was not able to detect any of them, whereas ESIDE-Depian achieved a 100% of success. Table 2 shows 15 packets labelled as potential negatives, this is, packets marked as positive (i.e., attack) by ESIDE-Depian but not by Snort. All of them correspond to the artificial anomalies we inserted and ESIDE-Depian was able to find the 100% of them. Table 3 shows some of the modified packets for the UDP-IP traffic tests
Examples of Anomalies
packit -t udp -s 127.0.0.1
-d 10.10.10.2 -o 0x10 -n 1
-T ttl -S 13352 -D 21763
packit -t udp -s 127.0.0.1
-d 10.10.10.2 -o 0x10 -n 0
-T ttl -S 13353 -D 21763
packit -t udp -s 127.0.0.1
-d 10.10.10.2 -o 0x50 -n 0
-T ttl -S 13352 -D 21763
Anomaly detection results
Potential false positives (anomalous packets)
2
Anomaly detection rate
100%
Table 3.
Anomaly Detection Tests for UDP-IP Traffic.
Again, in UDP-IP traffic Snort did not discover any anomaly, as expected. The 2 false positives reflected in table 3 belong again to the artificial anomalies fed by us (and crafted with PackIt). Table tbl:table4 summarises the results obtained with ICMP-IP traffic. Similarly to the previous cases, Snort failed to detect any of the attacks, whereas the 45 false positives that appear in table 4 are exactly the anomalies introduced by us in the traffic sample.
Examples of Anomalies
packit -i eth0 -t icmp -n 666
-s 3.3.3.3 -d 10.10.10.2
packit -i eth0 -t icmp -K 0
-s 3.3.3.3 -d 10.10.10.2
packit -i eth0 -t icmp -K 17
-C 0 -d 10.10.10.2
Anomaly detection results
Potential false positives (anomalous packets)
45
Anomaly detection rate
100%
Table 4.
Anomaly Detection Tests for ICMP-IP Traffic.
5.2 Connection Tracking and Payload Analysis
With the goal of evaluating these analysis capabilities of ESIDE-Depian in mind, we have followed a different strategy than in the case of header parameters: Snort is mainly focused on the analysis of the latter and covers the payload inspection by applying a set of regular expressions that do not provide any useful information to the Bayesian network (basically because it presents a different morpho-syntactical structure).
Moreover, the dynamic nature of the data these experts focus on, forces this change. Therefore, we have generated a brand new traffic sample to be used in the training phase. Then, only for test purposes, we have created yet another different one with some of its packet sequences modified by means of the tool NetDude (since PackIt only allows to change packets, not sequences).
Table 5 summarises the results achieved by ESIDE-Depian for the tests focused on the connection tracking and payload analysis.
Analysis Type
Connection Tracking
Payload Analysis
Analysed network packets
226,428
2,676
Attacks contained in sample
29
158
ESIDE-Depian hits
29
158
Table 5.
Connection Tracking and Payload Analysis Results.
6. Problems and solutions
This section gives account of the main problems that emerged during the design and test phase. More accurately, they were:
Integration of Snort: The first difficulty we faced was to find an effective way of integrating Snort in the system.
Our first attempt placed the verdict of Snort at the same level as those of the Bayesian experts in the Naive classifier. This strategy failed to capture the real possibilities of Bayesian networks since it simply added the information generated by Snort at the end of the process, more as a graft than a real integrated part of the model.
The key aspect in this situation was letting the Bayesian network absorb Snort’s knowledge to be able to actually replace it. Therefore, in the next prototype we recast the role of Snort as a kind of advisor, both in training and in working time.
In this way, the Bayesian experts use Snort’s opinion on the badness of incoming packets in the learning procedure and afterwards (as described in section 4) and manage to exceed Snort’s knowledge (Penya & Bringas, 2008).
Different parameter nature: The next challenge consisted on the different nature of the parameters that ESIDE-Depian has to control. Whereas TCP, UDP and ICMP are static and refer exclusively to one packet (more accurately to its header), the connection tracking and payload analysis experts are dynamic and require the introduction of the time notion.
In this way, the connection tracking expert checks if packets belong to an organised sequence of an attack (Estevez-Tapiador et al., 2003), so time is needed to represent predecessor and successor events. In a similar vein, the payload analysis expert must model state transitions between symbols and tokens that appear on it.
Therefore, in the same way that different tests had to be performed, we had to prepare an special traffic sample tailored to the kind of traffic those expert should focus to inspect
Disparity between good and bad traffic amount: Another problem to tackle was the composition of the traffic sample used to train the first group of experts (TCP, UDP, ICMP).
In order to help the acquisition of the initial reference knowledge in the training phase, the BN is fed with a traffic sample basically based on the attack-detection rules battery provided by Snort. Therefore, the training acquaints the BN with either kind of traffic simultaneously, good and bad.
Nevertheless, due to the disparity in the amount of packets belonging to one or another, traces containing attacks have to be fed several times (in the so-called presentation cycles) in order to let the BN learn to evaluate them properly.
Task parallelisation: Bayesian networks require many computational resources. Hence, several of the tasks to be performed were designed in a parallel way to accelerate it. For instance, the structural learning was devoted concurrently in 60 computers. In this way, the traffic sample (about 900.000 packets) was divided in blocks of 10.000 of packets that were processed with the PC-Algorithm. In addition, already on real-time, each expert was placed in a different machine not only to divide the amount of resources consumed but also to prevent from having a single point of failure.
False positives and false negatives: Finally, we coped with a usual problem related to anomaly detection systems: false positives (i.e. packets marked as potentially dangerous when they are harmless). In fact, minimising false positives is one of the pending challenges of this approach (Lundin, 2004).
Nevertheless, the double nature of ESIDE-Depian as anomaly and misuse detector reduces the presence of false positives to a minimum. False negatives, on the contrary, did threaten the system and, in this way, in the experiments accomplished in ESIDE-Depian, security was prioritized above comfort, so quantitative alarm-thresholds were set upon the production of the minimum false negatives, in spite of the false positive rates.
It is possible to find application domains, e.g., anti-virus software, in which false positive numbers are the target to be optimized, in order not to saturate the final user or the system administrator. Also in these cases ESIDE-Depian is able to manage the detection problem, simply by the specific setting up of the mentioned thresholds.
7. Related Work
Different approaches to develop network misuse detectors include expert systems (Alípio et al., 2003), intent-specification languages (Doyle et al., 2001), intelligent agent systems (Helmer et al., 2003) or rule-induction systems (Kantzavelou & Katsikas, 1997) (in (Kabiri & Ghorbani, 2005) the reader can obtain a detailed analysis of related work in this area).
Research in network anomaly detection has applied several well-known Artificial Intelligence paradigms such as support-vector machines (Mukkamala et al., 2005) or diverse data-mining-based approaches Lazarevic et al. (2003). Still, there is only one attempt to bring these two strands of work together.
More specifically, in Valdes & Skinner (2000), they achieve to combine anomaly and misuse but its analysis of network packets is too superficial to yield any good results in real life. In particular, despite the brilliant main contribution about integrating misuse-based and anomaly-based detection in one inherently unified and compact knowledge representation model, this work presents several shortcomings that prevent it from being applied in real scenarios: on the one hand, this approach only considers 7 detection parameters
Popular protocols as UDP connection-less protocol or the very-very problematic ICMP protocol are not taken into consideration. On the other hand, Bayesian Networks’ full capabilities are not really used. Thus, one of the most important topics provided by the Bayesian approach, the structural learning concept, is not definitively applied. Instead, they propose the Naive approach, which assumes the (unrealistic) hypothesis that there is no statistical dependence among the collection of detection parameters.
Finally, time notion does not play any role in the analysis model, even under the focus achieved over the TCP target protocol, which is, of course, connection-oriented and, so, chronological dependence among events is sure to appear.
8. Conclusions
As the use of Internet grows beyond all boundaries, the number of menaces rises to become subject of concern and increasing research. Against this, Network Intrusion Detection Systems (NIDS) monitor local networks to separate legitimate from dangerous behaviours. According to their capabilities and goals, NIDS are divided into misuse detection systems (which aim to detect well-known attacks) and anomaly detection systems (which aim to detect zero-day attacks). So far, no system to our knowledge combines advantages of both without any of their disadvantages. Moreover, the use of historical data for analysis or sequential adaptation is usually ignored, missing in this way the possibility of anticipating the behaviour of the target system.
ESIDE-Depian, a Bayesian-networks-based misuse and anomaly detection system. In another work, we detailed the composition of the Bayesian network, its training methodology and showed general performance results. Here we have focused on evaluating the integration of misuse and anomaly detection. To this end, we have adopted Snort (a well-known misuse detector) as misuse detector trainer so the Bayesian Network of five experts is able to react against both misuse and anomalies. The Bayesian experts are devoted to the analysis of different network protocol aspects and obtain the common knowledge model by means of separated Snort-driven automated learning process
Since ESIDE-Depian has passed the experiments brilliantly, it is possible to conclude that ESIDE-Depian using of Bayesian Networking concepts allows to confirm an excellent basis for paradigm unifying Network Intrusion Detection, providing not only stable Misuse Detection but also effective Anomaly Detection capabilities, with one only flexible knowledge representation model and a well-proofed inference and adaptation bunch of methods.
On the other hand, the Bayesian approach also enables to implement powerful features over it, such as Dynamic-Bayesian-Network-based full representation of time, in order to accomplish totally-characterised connection tracking and low level chronological event correlation, or explanation tracking of the inferred cause-effect reasoning processes. Furthermore, contrary to other approaches such as Neural Networks, Bayesian networks allow administrative managing of inner information structures, so specific relationships among packet detection parameters and final conclusion can be explained, in a white-box manner. Moreover, it is not only possible to recover reasoning information, but also to act on both Bayesian network structures and conditional probability parameters, in order to adjust the whole behaviour of the Network Intrusion Detection System to special needs or configurations.
Besides, dynamic regulation of knowledge representation model can be accomplished by using the sensibility analysis proposed by Castillo et al. (1996), so as to avoid denial of service attacks, automatically enabling or disabling expert modules by means of one combined heuristic measure which considers specific throughputs and representative features. In addition, it is also possible to perform model optimization, to obtain the minimal set of representative parameters, and also the minimal set of edges among them, with the subsequent increase of the general performance.
Moreover, approximate evidence propagation methods can also be applied, in order to improve inference and adaptation time of response. Current expert models only consider exact inference, but it is possible to find methods which provide fast responses, with only a small and affordable loss of accuracy.
In addition, Bayesian knowledge representation models present one further interesting capability in current Intrusion Detection state of art, the possibility to provide an ad-hoc method for IDS evaluation. Bayesian concept provides simulation of learned knowledge corresponding samples, so it is an ideal environment for artificial anomaly generation.
At last, also unifying of Host and Network Intrusion Detection paradigms can be accomplished at low level through the Dynamic Bayesian Network concept. Specifically, both sorts of event (i.e., basically, operating system syscalls and network packets) can be characterized in one single representation model, with a dynamic approach that can obtain, for example, the posterior probability of an exploitation of one specific host service due to one specific network packet (e.g. an Unix exec syscall from a shellcode inside a packet payload). Besides, not only inference can be afforded, but even prediction of next event, due
Future work will focus on further research on exploiting the aforementioned omni-directional inference capability of Bayesian networks to the prediction of the next event, as well as on comparing ESIDE-Depian to other cutting-edge intrusion detection systems
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1. Introduction
Rice (Oryza sativa L.) is the world’s second-most-important cereal crop. Nearly half of the world’s population utilizes it as a staple food. It belongs to the genus Oryza and family Poaceae, has 22 known species and has great economic importance [1]. These are adapted to variety of climatic conditions and can be grown both in dry and wetland habitats at high and low elevations. Green revolution substantially boosted rice productivity across Asia through a combination of modern new high yielding varieties and enhanced inputs like irrigation, fertilizer, and biocides [2]. Climate change is the only aspect that took away the breeder’s concern from productivity despite plateauing yield in most of the cereals over the past decade. To feed the burgeoning population of the world, especially in Asia where the population is predicted to climb from 4.3 to 5.2 billion by 2050, rice production needs to expand and the quality of the grains must to be improved [3, 4]. Global warming and climate change has been predicted to decline irrigated rice yields by around 4% by 2020 and ~ 7% by 2050, while rainfed rice yields are expected to decrease by 6% and marginally (2.5%) by 2050, respectively [5]. Abiotic stress like drought, salinity, and heat is the dominant drivers restricting agricultural growth and output of crops around the planet. Rice plants are sensitive to various abiotic stresses. Drought stress disrupts not just morphological features in rice plants, but also physiological and biochemical processes. it has been linked to a significant drop in crop yields due to detrimental effects on plant growth, physiology, and reproduction. Research studies tend to show that abiotic stress in combination of abiotic stress factors is the most harmful [6]. In rice, drought stress is the major limiting factor for rice production in both rainfed lowland (46 Mha) and upland (10 Mha) rice ecosystems across Asia [7, 8]. Soil salinity is rising expotentially with increasing sea levels in coastal areas and in irrigated lands where soluble salts washed away underwater are brought to ground level. Almost 40 Mha of rainfed lowland under rice cultivation in South and South-East Asia including India, Bangladesh, Myanmar, and Thailand, have been ravaged by unforeseen flash floods [9, 10]. Salinity stress affects the growth and development of rice plants at three different stages; germination, vegetative, and reproductive [11]. A major problem for stable rice production is high temperature in a number of tropical and sub-tropical countries, such as India, Bangladesh, China, Pakistan, Thailand, Sudan and many African countries. For the production of rice, atmospheric temperature at anthesis is crucial. Even 38°C, which is as such not high in tropical and subtropical countries, might cause substantial yield reduction due to pollen sterility [12]. The rice has emerged as a model genomic crop in the 21st century with its smaller genome size, high-quality genomic reference sequence, large genetic and genomic resources compared to every other crop. In the last 25 years a wide range of abiotic stress tolerance loci (QTLs) have been identified in rice [13]. The development of genome-wide DNA markers, i.e., simple sequence repeats, single nucleotide polymorphisms, and the identification of QTLs and marker trait associations, have prompted to new technologies in génomic and genetic engineering tolerant to various abiotic stress. Advancements in molecular biology techniques have enabled the discovery of many genes involved in abiotic stress tolerance through spatio-temporal gene expression analysis. Transgenic approaches have further validated functionally the identified candidate genes from the genetic expression analysis. Omics approaches such as genomics, proteomics, metabolomics, transcriptomics, epigenomics have emerged as powerful biotechnological tools, used for deciphering the abiotic stress responses as well as for producing climate-resilient abiotic stress-tolerant plants [14].
At present efforts are being made to identify several stress factors in the abiotic stress tolerance and to develop rice varieties with a tolerable stress through biotechnology, molecular breeding, genomics, transcriptomics, proteomics and metabolomics [15, 16, 17, 18]. Abiotic stresses such as drought, salinity, and heat affect the productivity of many agriculturally important crops. Therefore, to meet the food requirements of a growing world population, it is necessary to develop sustainable high-yielding varieties that can persist under abiotic stress [19]. This chapter highlights abiotic stresses such as drought, high temperatures, salinity as well as abiotic stress tolerance techniques in rice plants with emphasis on increased rice yields.
2. Major abiotic stresses affecting rice crop
Rice is the world’s most significant food crop, providing calories to more than half of the world’s population of 7 billion people. In most cultivable rice habitats, it has become increasingly vulnerable to losses induced by abiotic factors such as drought, floods, salt, heat, and cold. Drought or water shortages are the most damaging abiotic challenges for rice farming in rainfed habitats. Another important limitation affecting the rainfed lowlands is the submergence of rice plants for one to two weeks owing to flash floods. Long-duration cultivars are frequently affected by floods in the early phases of development, followed by drought during blooming, resulting in significant yield deficits [20]. Meanwhile, salinity, which is determined by a heavy concentration of soluble salts in the soil, is the second most common soil issue after drought, and it is a major constraint for rice production across the world. As a result of global climate change, heat stress is becoming a severe hazard to rice production. Heat stress hinders plant development, disrupts metabolic processes, and reduces output. Rice growing in temperate locations, as well as high-altitude conditions in tropical and subtropical zones, is hampered by low temperatures. Cold stress has a negative impact on rice crops throughout the germination, vegetative development, and reproductive phases, resulting in considerable production losses. Rice crops are sometimes subjected to numerous stressors (such as salt and drought, or drought followed by submergence), resulting in massive crop losses. Rice productivity would be significantly increased while water resources and soil quality were preserved if combined tolerance to several forms of abiotic stress was improved [21].
2.1 Drought stress
Drought is a severe abiotic stress that disrupts rice Morphological, Physiological, biochemical and molecular responces (Figure 1), resulting in considerable crop losses [22]. Drought stress has been linked to changes in plant length, biomass, and leaf area are associated with leaf senescence in a variety of crops, [23, 24] including rice (O. sativa) [25]. The occurrence of both drought and heat stresses in combination are more destructive (~70%) to crop production than other stresses occurring individually [26]. Drought stress causes oxidative stress by producing a buildup of reactive oxygen species in the chloroplast and mitochondria. Drought stress enhances the competitive ability of some weeds on crop plants by allowing them to use more water than crops [27]. The synergistic and antagonistic interactions between drought stress and pathogenic infection were similarly observed [28]. According to Wang et al. [29] drought before and after heading, has detrimental effects on brown and milled rice rates by influencing its quality to a great extent. It is the primary constraint to rice production in both rainfed lowland (46 Mha) and upland (10 Mha) rice ecosystems throughout Asia [7, 8]. Drought stress can alter tillering, floret initiation, and subsequent spikelet sterility, as well as grain filling, throughout vegetative growth, blooming, and the terminal period of rice cultivation [30]. Terminal drought is the most detrimental abiotic stress factor to rice grain yield [31]. Several studies on the effects of drought stress and dehydration revealed physiological acclimation of plants by altered antioxidant responses [32, 33]. Understanding the mechanisms that cause physiological responses to drought stress and dehydration conditions is critical. Drought tends to reduce the rate of cell division and expansion, leaf size, stem elongation, and root proliferation in rice crops, as well as interrupts stomatal opening and shutting periods, and plant nutrient and water absorption and utilization performance [34]. Deficiency of water and moisture in rice crops increases the rate of abscisic acid (ABA) biosynthesis which diminishes stomatal efficiency and conductance which reduces transpirational losses [35]. The complex nature of drought stress in rice and its strong interaction with the environment has slowed the breeding programs to develop drought adapted varieties.
Figure 1.
Drought stress induces various morphological, physiological, biochemical, and molecular responses in rice.
2.2 Salt stress
By the end of 2050, the world’s population will have risen to about nine billion people. On the other hand, due to the accumulation of high salt content in the soil, worldwide agricultural production would almost probably remain static, resulting in crop growth inhibition and eventual crop mortality. Salt stress is a worrisome phenomenon because it diminishes soil agricultural productivity, leading to lower crop yields [36]. Salinity is becoming a critical threat because of diminishing irrigation water quality. (Flowers [37]. Salinity and water logging afflict 23% and 37% of worldwide cultivated land, respectively, and it is estimated that about 20% of all cultivated land and nearly half of irrigated land is salt-affected, greatly reducing yield well below the genetic potential [38]. The problem of salinity is particularly intense to agriculture in South and Southeast Asia, which produces about 90% of the world’s rice [39]. By the middle of the twenty-first century, it is estimated that half of the cultivable land would be salt-affected [40]. It is suspected that the rise in soil salinity is due to poor irrigation water, its quality, and the use of brackish. Rice is a salinity-sensitive crop that performs poorly in soils with electrical conductivity (EC) as low as 3 dSm-1, however salty soil is commonly defined as EC >4 dSm-1 [41]. High-salt stress affects plants in several ways, such as ion toxicity, oxidative stress, alteration of metabolic processes, nutritional disorders, genotoxicity, membrane disorganization, reduction of cell division and expansion as well as water stress. In order to cope with the antagonistic effects of soil salinity some new strategies like remediation of salinized soils, to increase the salt tolerance of crop plants through traditional as well as molecular marker-assisted breeding techniques, and biotechnology should be adopted [37, 42, 43]. Khatun and Flowers [44] observed that salt toxicity adversely affect panicle length, spikelets per panicle, and 1000-grain weight in rice crop. Salinity also delays flowering and ripening and reduces the number of tillers, biomass, and leaf area in rice crops. According to Asch and Wopereis [45], irrespective of seasons and growth stages, salt toxicity reduces rice yield, the number of panicles, and grains and causes sterility in all rice cultivars.
2.3 Temperature stress
Heat stress, characterized by prevalence of high temperature is one of the major abiotic constraints for rice production, next only to drought and salinity [46]. Vastly increased greenhouse gases in the air are predicted to significantly affect the climate and worldwide average air temperature guesstimated to significantly raise by 1.4–5.8°C between 1990 and 2100 [47, 48]. Increasing temperature with this magnitude and severity reduced global rice production [49]. The reduction in rice yield is mainly attributable to changes in critical temperature at each specific phase of growth, namely, germination, seedling, rooting, leaf elongation, tillering, panicle initiation, primordia, panicle differentiation, anthesis and maturing [50]. One of the most sensitive phenological phases to extreme temperatures is pollination that leads to poor seed set and low grain quality [51, 52]. Rice is particularly vulnerable to heat stress during the reproductive and ripening stages, as simply a few hours of heat stress causes flowering plants to become sterile. High temperatures during ripening, on the other hand, might result in a decrease in milling quality and grain filling, resulting in reduced crop yield [53].
Increase in temperature, that leads to higher humidity, can cause spikelet sterility. Consequently, amid heat stress, the floral buds are unable to mobilize carbohydrates [53]. Low temperature stress is another similar environmental stress that can cause the plant’s development and growth to be slowed. Embrane structure and function, protein synthesis, and cellular cytoskeleton structure can all be severely affected by low temperature stress. Low temperatures also impede photosynthesis in both light and dark reactions; further, electron routes are disrupted, resulting in the formation of free radical species that can be harmful to rice crops, causing membrane deterioration. Japonica genotypes are more adaptable than indicas to cold temperatures and so are prevailing in high-altitude and latitude ecologies [54]. The respiratory rate of plants increase or decrease in accordance with the temperature, short-term low temperature stress leads to high respiration rate but in the case of long-term stress the cell gets damaged and eventually dies due to the reduced respiration rate.
3. Plant responses to abiotic stress
The signals of abiotic stress are a multi-faceted phenomenon due to a wide range of environmental abuses. Plants can produce appropriate responses that cause a particular change in conjunction with a specific stress condition, whereas there is significant overlap between abiotic signals. Typically, one sort of stress happens with or is followed by other stresses. The loss of water which is due to heat stress causes drought stress and in this way, both stresses are linked to each other. Signal perception is the first phase in a signal transduction pathway which is tracked by the production of secondary signals. Secondary signals can trigger a protein phosphorylation cascade, which can then control the activation of specific transcription factors (TFs) or target genes. Additionally, these signals can modify the quantity of secondary signals; as a result, more signaling molecules are produced, providing an extra checkpoint for signals to flow in a given direction. Till date, many signaling pathways have been reported [55].
3.1 ROS signaling
Oxygen is a two-edged sword for plants, since it is a necessary element to be able to develop normal growth, but unavoidably promotes the formation of of ROS like hydrogen peroxide (H2O2), superoxide radical, hydroxyl radical, singlet oxygen, etc. as a result of aerobic metabolic activities, such as photosynthesis and respiration. During stress circumstances in a plant, organelles such as mitochondria, peroxisomes, and chloroplast generate enormous amounts of ROS, which become highly corrosive and reactionary to nucleic acids, proteins, and lipids, inevitably leading to apoptosis or cellular damage [56].
Catalase (CAT),monodehydroascorbate reductase (MDHAR), superoxide dismutase (SOD), glutathione peroxidase (GPX), glutathione reductase (GR), dehydroascorbate reductase (DHAR), peroxiredoxin, and glutathione S transferase are all ROS foraging enzymes in plants (GST) [57]. These antioxidants work in the plant cells where they are present in different locations in order to detoxify ROS. The ROS homeostasis in plants must also be accompanied by non-enzymatic antioxidants, including tocophherols, Carotenoids, GSH, flavonoids and AsA [56]. Many other mechanisms, such as photosynthetic apparatus rearrangement, leaf movement, and leaf curling, can balance a plant’s energy absorption with CO2 supply, preventing ROS overproduction [57].
3.2 Phytohormone signaling
Phytohormones, a diverse set of signaling chemicals found in minute amounts inside cells, influence the responses. Plant growth, development, and nutrient allocation are all regulated by them. Responses to abiotic stress are triggered by a variety of stimuli, but phytohormones are responsible for the majority of them. Plants, which are sessile organisms, require phytohormones for survival [58]. They can carry out their function in their synthesis site or they can go to their action location [59]. Their family consist of cytokinin (CK), Indole acetic acid (IAA), salicylic acid (SA), ethylene, ABA, gibberellins (GAs), jasmonates, and brassinosteroids. The relatively newly discovered phytohormones are strigolactones. In regulating stress responses through interactions with some other hormones, ABA plays a major role. In order to regulate climate stress, it is the most exciting and indispensable hormone of the plant. In various phases of plant development, it plays a major role especially in opening and closing stomata, drought stress, seed germination and dormancy. PYR/PYL/RCAR-PP2C-SnRK2 is regarded as an ABA-generated signaling cascade, which effectively monitors seed dormancy even in occurrence of drought. ABA buildup amid drought stress restricts stomatal disclosure and modulates transpiration [60]. ABA signaling cascade consisted of 3 units, SnRK2/OST1 (Protein kinase), PP2C (protein phosphatases) and PYR/PYL/RCAR proteins have been investigated recently and their mechanism of operation was elucidated [61].
Ethylene is another major component of phytohormone. Ethylene is supposed to be the signaling path between plant growth and weather changes. Salinity, water logging, high temperature, frost, heavy metal interaction, nutritional inadequacy, and drought are examples of abiotic stressors that influence ethylene production [62]. Ethylene response factors (ERFs) in plants are members of a large family of transcription factors (TFs) that are activated in response to a variety of physiological and environmental stress.
3.3 Sos (salt overly sensitive) signaling
Salinity is the most important abiotic stress that diminishes rice crop productivity. Plants suffer from severe osmotic pressure and a scarcity of water as a result of salinity, which causes ions to accumulate in their tissues. Different rice varieties have different levels of tolerance [63]. IR29, for example, is a salt-sensitive species that accumulates 5–10 times more Na1 in the leaves than Pokkali or BK [64] . Salt tolerance in rice is achieved by limiting Na1 translocation. The gene SKC1/ HKT8 is responsible for salt tolerance and a strong K1/Na1 balance in the shoots, as well as decoding a HKT family Na1 selective transporter that regulates Na1 transport over vast distances [65]. HKT1 is a similar gene that is ubiquitously expressed in the roots and leaves and seems to be involved in the long-distance trafficking of Na1. The Na1/H1 counter transporter salt overly sensitive (SOS)1 can facilitate the outflow of Na1 across the plasma membrane [66]. Plants lacking SOS1 become particularly salt sensitive and their transfer of Na1 over long distances is hampered. The root tip epidermis and xylem parenchyma cells are the primary sites of SOS1 expression. At the root-soil interface, it extrudes too much Na1 from the root epidermal cells. SOS1 seems engaged in the Na1 translocation in the roots and shoots. It’s also responsible for providing ions from the xylem to the shoots in a manageable way. The SOS2/SOS3 kinase complex utilizes SOS1 as a substrate. Plants lacking SOS2 or SOS3 have the same salt sensitivity phenotype as SOS1 plants [64]. Ion homeostasis could be maintained as a result of this.
3.4 Calcium signaling
Calcium plays a role in a variety of abiotic stress symptoms, with varied degrees of directivity. Many of the functions in plants are governed by changes in intracellular calcium levels. Calcium is a harmful ion whose concentration in eukaryotic cells is controlled. Calcium ions travel through specific calcium ion channels, the cell membrane, or organelles into the cytosol after activation. Calcium ions have therefore been progressively increased to provide a number of final preparations for calcium-dependent proteins such as calmodulin, CDPCs and calmodulin-dependent phosphatases. Local calcium increases may also occur in specific organelles, such as chloroplasts, and can easily govern specific actions in organelles [67]. The expanded accurate determination of calcium as a result of abiotic stress, their association with downstream end progressions, and the use of calcium ion homeostasis inhibitors, however, highlight its importance.
4. Biotechnological approach for improving major abiotic stress tolerance in rice
4.1 Genetic engineering
The biotechnological approach is an appealing complement to traditional strategies for improving rice genotypes because it allows for the stacking of more genes into the genome without disrupting their genetic background [68]. Drought resistance was greatly improved by overexpressing SNAC1 (STRESS RESPONSIVE NAC 1) in rice, with 22–34 percent higher seed setting than control conditions in the field under acute drought stress during the reproductive stage, with no yield penalty or phenotypic alterations [36]. Similarly, under extreme field drought circumstances, overexpression of AP37 under the control of the OsCc1 promoter enhanced drought, salinity, and cold tolerance at the vegetative stage and also gave a 16–57 percent yield advantage over the control at the reproductive stage [69]. At the vegetative stage, overexpression of OsNAC10 with the GOS2 and RCc3 (root-specific) promoters improved drought tolerance, as well as high salinity and cold tolerance. RCc3:OsNAC10 transgenic rice cultivar showed yield advantages of 25–42 percent in the field under drought conditions [70]. OsPYL/RCAR5 (cytosolic ABA receptor) in rice plants functions as a positive inducer of abiotic stress-responsive genes [17, 18]. In contrast, rice plants exhibited a quick accumulation of soluble sugars, which act as interoperable solutes/osmoprotectants, lead to delays leaf drying and rolling [71]. Heat stress-induced gene expression and metabolite synthesis boost crop plant tolerance markedly [72]. HSFs function as molecular sensors to directly sense ROS such as H2O2 and control the expression of oxidative stress response genes during oxidative stress [73]. Binding of HSFs with heat shock elements (nGAAn) present in the heat responsive genes, including HSPs is critical for transcription induction of HSGs otherwise called heat shock response [74, 75, 76]. The enhanced expression of HSP70 assists in the translocation, proteolysis, translation, folding, aggregation, and refolding of denatured proteins [77]. HSP70 chaperones interact with a wide spectrum of proteins, particularly unfolded proteins generated in stressful situations [78]. Rice has 25 HSFs on 10 chromosomes other than chromosomes 11 and 12. Of these, 13 genes are class A, 8 are class B, and the remaining 4 are class C type HSFs [79]. Two HSBPs, namely OsHSBP1 and OsHSBP2, existing in rice plants and are abundantly expressed in all tissues under ordinary conditions, involved with HSR regulation, seed growth and found in considerably greater amount after heat shock recovery [80]. While considerable progress has been made in clarifying thermotolerance molecular systems, how plants perceive and translate heat stress signals is still not easy.
4.2 Marker-assisted breeding
Abiotic stress tolerance alleles were genetically eroded as a result of domestication and breeding for high yield. As a result, efforts are currently being conducted to restore allelic diversity for abiotic stress tolerance in modern high yielding varieties using locally adapted cultivars and germplasm. Stress sensitive genotypes/parents have contributed many advantageous alleles for abiotic stress tolerance, indicating the impact of a genotype’s genetic background on its performance under stress [81]. A comprehensive screening and evaluation process, gene genetic background interaction, and gene environment interaction are all important factors in the utilization of QTLs in abiotic stress tolerance. The combination of whole genome expression data, QTL information, and meta-QTL analysis has proven to be a useful approach for narrowing down the search for abiotic stress tolerance candidate genes [82]. There are many success stories of introgression of QTLs for abiotic stress tolerance, and many varieties are in the advanced field trails stage [83] for tolerance to drought, salinity, and heat separately or in combination.
IRRI revealed the first important and persistent QTLs for grain yield under extreme drought stress [84]. Vikram et al [85] studied three populations: N22/IR64, N22/MTU1010, and N22/Swarna, and discovered a major consistent grain yield QTL, qDTY1.1, on chromosome 1 that can be used for marker-assisted breeding (MAB). Furthermore, in Vandana/IR64 populations, qDTY1.1 and the locus for plant height (sd1) were shown to be connected [86], suggesting that in large segregating populations, recombinant alleles with unlinked qDTY1.1 and sd1 could create drought-tolerant plants with shorter stature [87]. In Apo/Swarna, Apo/IR72, and Vandana/IR72 genetic backgrounds, another large QTL “qDTY6.1” [88] was found on chromosome 6, explaining 40–66 per cent of the genetic variation for grain yield in aerobic conditions. Swarna and IR72, both drought-prone, performed better in aerobic conditions when this QTL was present. This was also the first report of a significant QTL that increases yield and yield potential in aerobic circumstances. Nevertheless, this QTL had no effect on lowland drought stress conditions. Three grain yield QTLs under drought stress namely qDTY2.2, qDTY3.1, and qDTY12.1 were introgressed into high quality Malaysian rice cultivar MRQ74 by MAB [89]. An Indian project in collaboration with IRRI: “From QTL to variety: marker assisted breeding of abiotic stress tolerant rice varieties with major QTLs for drought, submergence and salt tolerance” has introgressed seven consistent QTLs for grain yield under drought into high yielding, submergence-tolerant elite backgrounds of Swarna-Sub1, Samba Mahsuri-Sub1, and IR64-Sub1 [83].
Saltol QTL is a key salt-tolerant QTL that has been widely exploited to create excellent rice cultivars around the world Lin et al. [90] used an F2 population resulting from a hybrid between “Nona Bokra” and “Koshihikari” to find multiple QTLs for Na1 and K1 absorption in shoots and roots, including a significant QTL responsible for SKC1 on chromosome 1. Ren et al. [63] cloned the SKC1 QTL, which maintains K1 homeostasis in salt-tolerant cultivars under salt stress, and the SKC1 gene, which is a member of the HKT-type transporters and corresponds to the OsHKT8/Os01g0307500 locus. Using F2 mapping populations, Zhou et al. [91] and Deng et al. [92] mapped QTLs qSKC-1 and qSNC-1 for SKC and SNC, respectively, between SSR markers RM283 and RM312. Deng et al. [93] used rice salt-tolerant 1 (rst1) mutant and showed that rst1 was controlled by a single recessive gene and QTL mapping between rst13Peiai 64 revealed the QTL loci on chromosome 6. Bizimana et al. [94] identified QTLs using RILs derived from IR29 (a salt-sensitive line) and Hasawi (a salt-tolerant line) and could not find Saltol or QTLs nearby this position indicating that tolerance in Hasawi is due to novel QTLs other than Saltol/SKC1. Emon et al. [95] and Kumar et al. [96] used association panel following a genome-wide association study approach to find marker-trait associations for salt stress tolerance. Kumar et al. [96] discovered 20 SNPs (loci) that were strongly related with Na1/K1 ratio at the reproductive stage, as well as the Saltol region, which is known to affect salt tolerance at the seedling stage. Many notable examples of transferring the Saltol QTL into elite rice varieties by MABC include PB1121 and PB6 [97], AS996 [98, 99], Bac Thom 7 [100, 101], Binadhan-7 [102], BRRI Dhan [103].
4.3 Omics approaches
Technological advancement in the omics area, the intrinsic genes for complicated abiotic stress in plants might be elucidated [14, 104]. Since high-strength omics approaches produced huge numbers of data, requiring both computer tools and storage resources, and data analysis, several online databases, servers and platforms were developed [105]. Proteomics and metabolomics have been shown to grow rapidly, allowing researchers to get extensive and accurate information on plant cell produced proteins and metabolites in response to environmental concerns [14, 106]. Both these emerging areas are highly expected to improve cereal crops. Similarly, profiling transcriptomics is extremely useful in ensuring a thorough understanding of regulatory molecules and their networks that are important to the communication of stress tolerance [106]. For illustrate, in order to learn more about regulatory processes and identify stress-responsive transcripts, researchers compared transcripts from tolerant and sensitive rice cultivars [107]. Despite significant improvements in high-throughput genotyping, phenotyping of complex abiotic stress responses (sometimes multigenic) remains a difficult task for molecular breeders [108]. Plants’ epigenetic regulators have emerged as important regulatory mechanisms for responding to and inducing tolerance to abiotic stressors [109]. Epigenetic modulation of plant abiotic stress responses has been revealed thanks to breakthroughs in epigenomics. Short non-coding RNAs, such as miRNA, have emerged as critical epigenetic regulators of plant responses to stress [109]. However, more research is needed into how key crops, including rice, respond to abiotic stress, particularly at the epigenetic level. Overall, multiple omics techniques provide good platforms for understanding insights into plant responses and adaptation mechanisms, as well as developing abiotic stress tolerant, smart crops.
5. Perspectives and conclusion
Considering the massive losses of crop production due to severe environmental stresses, the development of crop varieties with increased tolerance or resistance to multiple stresses is presently indispensable. To date, relatively few genes reported to react to abiotic stress because agronomic characteristics of these stress tolerances have been controlled by many QTL, which show low inheritance and substantial interactions between the G/E systems. The discovery of possible genes for sustaining high pollen viability, effective gamete fertilization, and seed development in harsh conditions is not far off. With the progress of high-throughput techniques, many genes which are involved in stress regulation have been identified [110]. Plants that are subjected to several abiotic challenges at the same time must be investigated in order to comprehend the impact of various stresses. Every new combination of stresses has been suggested as a special type of stress, because it generates a totally new appropriate response. Enhanced and implementing tolerance mechanisms with the use of available low-cost sequencing and genotyping platforms, genetic and genomic resources and transgenic approaches provide huge opportunities for better rice cultivars in the near-coming future. Breeding and marker assistant selection, as well as modifying stress responses via plant hormones, are all ways that can be used to manage abiotic stress responses. Plant functional genomics perspectives which including proteomics, transcriptomic, and metabolomic analysis, as well as other high-throughput approaches and technologies, have yielded a number of drought-regulated genes, transcription factors, and cellular signaling components whose functions are crucial in rice stress tolerance.
\n',keywords:"Abiotic stress, QTL mapping, phytohormones, omices",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/77558.pdf",chapterXML:"https://mts.intechopen.com/source/xml/77558.xml",downloadPdfUrl:"/chapter/pdf-download/77558",previewPdfUrl:"/chapter/pdf-preview/77558",totalDownloads:180,totalViews:0,totalCrossrefCites:0,dateSubmitted:"May 28th 2021",dateReviewed:"June 15th 2021",datePrePublished:"September 21st 2021",datePublished:"January 26th 2022",dateFinished:"July 14th 2021",readingETA:"0",abstract:"Rice, world’s second major, staple cereal crop that feed the more than 50% of world’s population. To safeguard the production and to fulfill the demand of ever the increasing population and urbanization there is need to increase the rice production. Though the rice yield has increased due to the development of modern technology and climate resilient high yielding cultivars but still it is 10–15 per cent lower than its potential yield due to various biotic and abiotic stress. Drought, extreme temperature, salinity, harmful radiation, heavy metals, gaseous pollutants are the most detrimental abiotic stresses factors that cause the morphological, physiological and biochemical changes in the rice crops and ultimate result is the reduction of rice production globally. Tolerance against these stresses through exploitation of potent biotechnological tools, molecular markers, QTL mapping omices approaches, phytohormones which could offer a more adequate and rapid solution to overcome these abiotic stresses and to enhance the ultimate grain yield of rice.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/77558",risUrl:"/chapter/ris/77558",signatures:"Manoj Kumar, Sandhya, Pawan Kumar, Akash Gaurav Singh and Aravind Kumar Jukanti",book:{id:"10896",type:"book",title:"Integrative Advances in Rice Research",subtitle:null,fullTitle:"Integrative Advances in Rice Research",slug:"integrative-advances-in-rice-research",publishedDate:"January 26th 2022",bookSignature:"Min Huang",coverURL:"https://cdn.intechopen.com/books/images_new/10896.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-600-8",printIsbn:"978-1-83969-599-5",pdfIsbn:"978-1-83969-601-5",isAvailableForWebshopOrdering:!0,editors:[{id:"189829",title:"Dr.",name:"Min",middleName:null,surname:"Huang",slug:"min-huang",fullName:"Min Huang"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"328488",title:"Dr.",name:"Dr Manoj",middleName:null,surname:"Kumar",fullName:"Dr Manoj Kumar",slug:"dr-manoj-kumar",email:"manoj.vishnoi108@gmail.com",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/328488/images/16690_n.jpg",institution:null},{id:"350988",title:"Dr.",name:"Sandhya",middleName:null,surname:"Kulhari",fullName:"Sandhya Kulhari",slug:"sandhya-kulhari",email:"sandhya.kulhari1990@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"414810",title:"Dr.",name:"Pawan",middleName:null,surname:"Kumar",fullName:"Pawan Kumar",slug:"pawan-kumar",email:"pawanchoudhary2@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"423316",title:"Dr.",name:"Aravind Kumar",middleName:null,surname:"Jukanti",fullName:"Aravind Kumar Jukanti",slug:"aravind-kumar-jukanti",email:"Arvind.Jukanti@icar.gov.in",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Indian Institute of Rice Research",institutionURL:null,country:{name:"India"}}},{id:"423317",title:"Dr.",name:"Akash Gaurav",middleName:null,surname:"Singh",fullName:"Akash Gaurav Singh",slug:"akash-gaurav-singh",email:"Singhag@uasd.in",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. Major abiotic stresses affecting rice crop",level:"1"},{id:"sec_2_2",title:"2.1 Drought stress",level:"2"},{id:"sec_3_2",title:"2.2 Salt stress",level:"2"},{id:"sec_4_2",title:"2.3 Temperature stress",level:"2"},{id:"sec_6",title:"3. Plant responses to abiotic stress",level:"1"},{id:"sec_6_2",title:"3.1 ROS signaling",level:"2"},{id:"sec_7_2",title:"3.2 Phytohormone signaling",level:"2"},{id:"sec_8_2",title:"3.3 Sos (salt overly sensitive) signaling",level:"2"},{id:"sec_9_2",title:"3.4 Calcium signaling",level:"2"},{id:"sec_11",title:"4. Biotechnological approach for improving major abiotic stress tolerance in rice",level:"1"},{id:"sec_11_2",title:"4.1 Genetic engineering",level:"2"},{id:"sec_12_2",title:"4.2 Marker-assisted breeding",level:"2"},{id:"sec_13_2",title:"4.3 Omics approaches",level:"2"},{id:"sec_15",title:"5. Perspectives and conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Bajaj, S., Mohanty, A., 2005. 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Available from: https://doi.org/10.1016/S0074-7696(08)62707-2'},{id:"B68",body:'Jain, R.K., Jain, S., 2000. Transgenic strategies for genetic improvement of basmati rice. Indian J. Exp. Biol. 38 (1), 6_17'},{id:"B69",body:'Kim, Y.S., Kim, J.K., 2009. Rice transcription factor AP37 involved in grain yield increase under drought stress. Plant signal Behav. 4 (8), 735_736'},{id:"B70",body:'Jeong, J.S., Kim, Y.S., Baek, K.H., Jung, H., Ha, S.H., Do Choi, Y., et al., 2010. Root-specific expression of OsNAC10 improves drought tolerance and grain yield in rice under field drought conditions. Plant Physiol. 1531, 185_197'},{id:"B71",body:'Abdula, S.E., Lee, H.J., Kim, J., Nin˜o, M.C., Jung, Y.J., Cho, Y.C., et al., 2016. BrUGE1 transgenic rice showed improved growth performance with enhanced drought tolerance. Breed Sci. 66 (2), 226_233'},{id:"B72",body:'Hasanuzzaman, M., Nahar, K., Alam, M.M., Roychowdhury, R., Fujita, M., 2013. 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Functional analysis of OsHSBP1 and OsHSBP2 revealed their involvement in the heat shock response in rice (Oryza sativa L.). J. Exp. Bot. 63, 6003_6016'},{id:"B81",body:'Tiwari, S., Krishnamurthy, S.L., Kumar, V., Singh, B., Rao, A.R., Mithra, S.V.A., et al., 2016. Mapping QTLs for salt tolerance in rice (Oryza sativa L.) by bulked segregant analysis of recombinant inbred lines using 50K SNP chip. PLoS ONE 11 (4), e0153610. Available from: https://doi.org/10.1371/journal. pone.0153610'},{id:"B82",body:'Sandhu, M., Sureshkumar, V., Prakash, C., Dixit, R., Solanke, A.U., Sharma, T.R., et al., 2017. RiceMetaSys for salt and drought stress responsive genes in rice: A web interface for crop improvement. BMC bioinformatics 18 (1), 432. Available from: https://doi.org/10.1186/s12859-017-1846-y'},{id:"B83",body:'Singh, R., Singh, Y., Xalaxo, S., Verulkar, S., Yadav, N., Singh, S., et al., 2016. From QTL to varietyharnessing the benefits of QTLs for drought, flood and salt tolerance in mega rice varieties of India through a multi-institutional network. Plant Sci. 242, 278_287'},{id:"B84",body:'Bernier, J., Kumar, A., Venuprasad, R., Spaner, D., Atlin, G.N., 2007. A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Sci. 47, 507_516'},{id:"B85",body:'Vikram, P., Swamy, B.P.M., Dixit, S., Ahmed, H.U., Teresa, M., Cruz, S., 2011. Reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet. 12, 1_15'},{id:"B86",body:'Venuprasad, R., Bool, M.E., Quiatchon, L., Cruz, M.S., Amante, M., Atlin, G.N., 2012a. A large-effect QTL for rice grain yield under upland drought stress on chromosome 1. Mol. Breed. 30 (1), 535_547'},{id:"B87",body:'Vikram, P., Swamy, B.M., Dixit, S., Trinidad, J., Cruz, M.T.S., Maturan, P.C., et al., 2016. Linkages and interactions analysis of major effect drought grain yield QTLs in rice. PLoS ONE 11. Available from: Https:// doi.org/10.1371/journal.pone.0151532'},{id:"B88",body:'Venuprasad, R., Bool, M.E., Quiatchon, L., Atlin, G.N., 2012b. A QTL for Rice Grain Yield in Aerobic Environments'},{id:"B89",body:'Shamsudin, N.A.A., Swamy, B.P.M., Ratnam, W., Sta. Cruz, M.T., Sandhu, N., Raman, A.K., et al., 2016. Pyramiding of drought yield QTLs into a high quality Malaysian rice cultivar MRQ74 improves yield under reproductive stage drought. Rice 9, 21. Available from: https://doi.org/10.1186/s12284-016-0093-6'},{id:"B90",body:'Lin, H.X., Zhu, M.Z., Yano, M., Gao, J.P., Liang, Z.W., Su, W.A., et al., 2004. QTLs for Na1 and K1 uptake of the shoots and roots controlling rice salt tolerance. Theor. Appl. Genet. 108 (2), 253_260'},{id:"B91",body:'Zhou, J., Wang, F., Deng, P., Jing, W., Zhang, W., 2013. Characterization and mapping of a salt-sensitive mutant in rice (Oryza sativa L.). J. Integr. Plant Biol. 55 (6), 504_513'},{id:"B92",body:'Deng, P., Shi, X., Zhou, J., Wang, F., Dong, Y., Jing, W., et al., 2015b. Identification and fine mapping of a mutation conferring salt-sensitivity in rice (Oryza sativa L.). Crop Sci. 55, 219_228'},{id:"B93",body:'Deng, P., Jiang, D., Dong, Y., Shi, X., Jing, W., Zhang, W., 2015a. Physiological characterisation and fine mapping of a salt-tolerant mutant in rice. Funct. Plant Biol. 42 (11), 1026_1035'},{id:"B94",body:'Bizimana, J.B., Luzi-Kihupi, A., Murori, R.W., Singh, R.K., 2017. Identification of quantitative trait loci for salinity tolerance in rice (Oryza sativa L.) using IR29/Hasawi mapping population. J. Genet. 96 (4), 571_582'},{id:"B95",body:'Emon, R.M., Islam, M.M., Halder, J., Fan, Y., 2015. Genetic diversity and association mapping for salinity tolerance in Bangladeshi rice landraces. Crop J. 3, 440_444'},{id:"B96",body:'Kumar, V., Singh, A., Mithra, S.V.A., Krishnamurthy, S.L., Parida, S.K., Jain, S., et al., 2015. Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Res 22 (2), 133_145. Available from: https://doi.org/10.1093/dnares/dsu046'},{id:"B97",body:'Singh, R.K., Gopalakrishnan, S., Singh, V.P., Prabhu, K.V., Mohapatra, T., Singh, N.K., et al., 2011. Marker assisted selection: A paradigm shift in basmati breeding. Indian J. Genet. 71, 120_128'},{id:"B98",body:'Cuc, L.M., Quyen, P.T., Huyen, L.T.N., Ham, L.H., 2015. Breeding for the salinity tolerance rice variety in Vietnam. J. Agric. Technol. 11 (8), 2263_2272'},{id:"B99",body:'Huyen, N.T.L., Cuc, M.L., Ismail, A.M., Ham, H.L., 2012. Introgression the salinity tolerance QTLs saltol into AS996, the elite rice variety of Vietnam. Am. J. Plant Sci. 3, 981_987'},{id:"B100",body:'Linh, H.L., Linh, H.T., Xuan, D.T., Ham, H.L., Ismail, A.M., Khanh, D.T., 2012. Molecular breeding to improve salt tolerance of rice (Oryza sativa L.) in the red river delta of Vietnam. Int. J. Plant Genom 2012. Available from: https://doi.org/10.1155/2012/949038'},{id:"B101",body:'Vu, H.T.T., Le, D.D., Ismail, A.M., Le, H.H., 2012. Marker-assisted backcrossing (MABC) for improved salinity tolerance in rice (Oryza sativa L.) to cope with climate change in Vietnam. Aust. J. Crop Sci. 6, 1649_1654'},{id:"B102",body:'Mondal, U., Khanom, M.S.R., Hassan, L., Begum, S.N., 2013. Foreground selection through SSRs markers for the development of salt tolerant rice variety. J. Bangladesh Agric. Univ. 11, 67_72. Available from: https://doi.org/10.3329/jbau.v11i1.18215'},{id:"B103",body:'Ho, V.T., Thomson, M.T., Ismail, A.M., 2016. Development of salt tolerant IR64 near isogenic lines through marker-assisted breeding. J. Crop Sci. Biotechnol. 19 (5), 373_381'},{id:"B104",body:'Kujur, A., Bajaj, D., Upadhyaya, H.D., Das, S., Ranjan, R., Shree, T., et al., 2015. Employing genome-wide SNP discovery and genotyping strategy to extrapolate the natural allelic diversity and domestication patterns in chickpea. Front. Plant Sci. 6, 162'},{id:"B105",body:'Deshmukh, R., Sonah, H., Patil, G., Chen, W., Prince, S., Mutava, R., et al., 2014. Integrating omic approaches for abiotic stress tolerance in soybean. Front. Plant Sci. 5, 244'},{id:"B106",body:'Ramalingam, A., Kudapa, H., Pazhamala, L.T., Weckwerth, W., Varshney, R.K., 2015. Proteomics and metabolomics: Two emerging areas for legume improvement. Front. Plant Sci. 6, 1116'},{id:"B107",body:'Shankar, R., Bhattacharjee, A., Jain, M., 2016. Transcriptome analysis in different rice cultivars provides novel insights into desiccation and salinity stress responses. Sci. Rep. 6, 23719. Available from: https://doi.org/10.1038/srep23719'},{id:"B108",body:'Kuijken, R.C.P., van Eeuwijk, F.A., Marcelis, L.F.M., Bouwmeester, H.J., 2015. Root phenotyping: From component trait in the lab to breeding. J. Exp. Bot. 66, 5389_5401'},{id:"B109",body:'Kumar, V., Khare, T., Shriram, V., Wani, S.H., 2017. Plant small RNAs: The essential epigenetic regulators of gene expression for salt-stress responses and tolerance. Plant Cell Rep. 37, 61_75'},{id:"B110",body:'Gujjar, R.S., Akhtar, M., Singh, M., 2014. Transcription factors in abiotic stress tolerance. Indian J. Plant Physiol. 19, 306_316. Available from: https://doi.org/10.1007/s40502-014-0121-8'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Manoj Kumar",address:"manoj.vishnoi108@gmail.com",affiliation:'
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The issue is particularly relevant due to the stricter regulations regarding exhaust gas purity. A brief review of the methods for reducing Hg and NOx emissions has been made, pointing out their pros and cons. Against this background, the results of the authors’ own research on the injection of selected oxidants into flue gases to remove both of these pollutants are presented. The injection of sodium chlorite solution into the flue gas (400 MWe lignite fired unit) upstream the wet flue gas desulphurization (WFGD) absorber contributed to the oxidation of both metallic mercury and nitric oxide and enhanced their removal efficiency. The results of tests on lignite and hard coal flue gases indicate that in order to reduce the unfavorable phenomenon of mercury re-emission from WFGD absorbers, in some cases, it is necessary to add selected chemical compounds (e.g., sulfides) to the desulfurization system. The results of field tests for flue gas from lignite (400 MWe unit) and hard coal-fired boilers (195 and 220 MWe units) confirmed the usefulness of oxidizer injection technology to reduce mercury emissions below the level required by BAT conclusions.",signatures:"Maria Jędrusik, Dariusz Łuszkiewicz and Arkadiusz Świerczok",authors:[{id:"317074",title:"Prof.",name:"Maria",surname:"Jedrusik",fullName:"Maria Jedrusik",slug:"maria-jedrusik",email:"maria.jedrusik@pwr.edu.pl"},{id:"317075",title:"Dr.",name:"Dariusz",surname:"Luszkiewicz",fullName:"Dariusz Luszkiewicz",slug:"dariusz-luszkiewicz",email:"dariusz.luszkiewicz@pwr.edu.pl"},{id:"317076",title:"Prof.",name:"Arkadiusz",surname:"Swierczok",fullName:"Arkadiusz Swierczok",slug:"arkadiusz-swierczok",email:"arkadiusz.swierczok@pwr.edu.pl"}],book:{id:"10178",title:"Environmental Emissions",slug:"environmental-emissions",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"40700",title:"Dr.",name:"Aniela",surname:"Matuszewska",slug:"aniela-matuszewska",fullName:"Aniela Matuszewska",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"I graduated as a chemist specializing in organic chemistryI(Silesian University, Katowice). I defended my doctorate in hard coal physicochemistry (Technical University Wrocław) and then I prepared my post -doctoral work in the domain ofgeochemistry of fossil organic matter . I am continuing research in this area . I am also interested in the domain of environmental protection.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},{id:"40798",title:"Prof.",name:"Maria",surname:"Czaja",slug:"maria-czaja",fullName:"Maria Czaja",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},{id:"316730",title:"Dr.",name:"David Galan",surname:"Madruga",slug:"david-galan-madruga",fullName:"David Galan Madruga",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto de Salud Carlos III",institutionURL:null,country:{name:"Spain"}}},{id:"316784",title:"Dr.",name:"Rakhi",surname:"Tailor",slug:"rakhi-tailor",fullName:"Rakhi Tailor",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"317074",title:"Prof.",name:"Maria",surname:"Jedrusik",slug:"maria-jedrusik",fullName:"Maria Jedrusik",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"317076",title:"Prof.",name:"Arkadiusz",surname:"Swierczok",slug:"arkadiusz-swierczok",fullName:"Arkadiusz Swierczok",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"317989",title:"M.Sc.",name:"Farzaneh",surname:"Hajirasouliha",slug:"farzaneh-hajirasouliha",fullName:"Farzaneh Hajirasouliha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Northumbria University",institutionURL:null,country:{name:"United Kingdom"}}},{id:"319806",title:"Prof.",name:"Yogesh Kumar",surname:"Vijay",slug:"yogesh-kumar-vijay",fullName:"Yogesh Kumar Vijay",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"319843",title:"Dr.",name:"Minal",surname:"Bafna",slug:"minal-bafna",fullName:"Minal Bafna",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"320479",title:"Dr.",name:"Dominika",surname:"Zabiegaj",slug:"dominika-zabiegaj",fullName:"Dominika Zabiegaj",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Northumbria University",institutionURL:null,country:{name:"United Kingdom"}}}]},generic:{page:{slug:"waiver-policy",title:"Waiver Policy",intro:"
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",metaTitle:"Waiver Policy",metaDescription:"We feel that financial barriers should never prevent researchers from publishing their research. With the need to make scientific research more publically available and support the benefits of Open Access, more institutions and funders have dedicated funds to assist their faculty members and researchers cover the APCs associated with publishing in Open Access. Below we have outlined several options available to secure financing for your Open Access publication.",metaKeywords:null,canonicalURL:"/page/waiver-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"
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The first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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However, as Open Access becomes a more commonly used publishing option for the dissemination of scientific and scholarly content, in addition to institutions, there are a growing number of funders who allow the use of grants for covering OA publication costs, or have established separate funds for the same purpose.
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Please consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
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IntechOpen Waivers in Action
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For Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
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Our mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
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While providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
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How to Apply for a Waiver
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The application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
Feel free to contact us at funders@intechopen.com if you have any questions about Funding options or our Waiver program. If you have already begun the process and require further assistance, please contact your Author Service Manager, who is there to assist you!
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Note: All data represented above was collected by IntechOpen from 2013 to 2017.
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Important global efforts have been made to eliminate malaria leading to significant reduction in malaria cases and mortality in Africa by 42% and 66%, respectively. Early diagnosis, improved drug therapies and better health infrastructure are key components, but this extraordinary success is mainly due the use of long-lasting insecticidal nets (LLINs) and indoor residual sprayings (IRS) of insecticide. Unfortunately, the emergence and spread of resistance in mosquito populations against insecticides is jeopardising the effectiveness of the most efficient malaria control interventions. To help establish suitable resistance management strategies, it is vital to better understand the distribution of resistance, its mechanisms and impact on effectiveness of control interventions and malaria transmission. In this chapter, we present the current status of insecticide resistance worldwide in main malaria vectors as well as its impact on malaria transmission, and discuss the molecular mechanisms and future perspectives.",book:{id:"6339",slug:"towards-malaria-elimination-a-leap-forward",title:"Towards Malaria Elimination",fullTitle:"Towards Malaria Elimination - A Leap Forward"},signatures:"Jacob M. Riveron, Magellan Tchouakui, Leon Mugenzi, Benjamin D.\nMenze, Mu-Chun Chiang and Charles S. 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It conceptualizes health promotion from a multifaceted and multi-professional perspective hinged on the empowerment of communities and individuals to play active roles and adopt behaviour consistent with the goals of good health. The paper drawing on documentary data sees health promotion as critical to the achievement of health goals in the continent and equally reflects on the theories of health promotion, strategies for health promotion and challenges to health promotion in Africa. 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The purpose of this article is to outline the scope of low health literacy as a concept and explore some appropriate interventions that researchers and healthcare professionals may use to reduce its negative impact on health outcomes such as mortality. The authors conclude by identifying areas of research that are needed to advance the conceptualization of health literacy in reducing hospital mortality and morbidity.",book:{id:"7158",slug:"strategies-to-reduce-hospital-mortality-in-lower-and-middle-income-countries-lmics-and-resource-limited-settings",title:"Strategies to Reduce Hospital Mortality in Lower and Middle Income Countries (LMICs) and Resource-Limited Settings",fullTitle:"Strategies to Reduce Hospital Mortality in Lower and Middle Income Countries (LMICs) and Resource-Limited Settings"},signatures:"Monique Ann-Marie Lynch and Geovanni Vinceroy Franklin",authors:[{id:"276834",title:"Dr.",name:"Monique",middleName:"Ann-Marie",surname:"Lynch",slug:"monique-lynch",fullName:"Monique Lynch"},{id:"289629",title:"MSc.",name:"Geovanni",middleName:null,surname:"Franklin",slug:"geovanni-franklin",fullName:"Geovanni Franklin"}]},{id:"69468",title:"The Global Burden and Perspectives on Non-Communicable Diseases (NCDs) and the Prevention, Data Availability and Systems Approach of NCDs in Low-resource Countries",slug:"the-global-burden-and-perspectives-on-non-communicable-diseases-ncds-and-the-prevention-data-availab",totalDownloads:2760,totalCrossrefCites:7,totalDimensionsCites:19,abstract:"The burden of non-communicable diseases (NCDs) is growing swiftly in low-resourced countries resulting in deleterious health resembling the NCDs burden in high-resourced countries. Despite the availability of information on the escalating adverse economic and health effects of NCDs globally, specific strategies designed to address the growing burden of NCDs in low-resourced countries remain substandard. Research engines like EBSCOhost, Science Citation Index, CINAHL database, PsycINFO, Cochrane Database of Systematic Reviews, published and unpublished abstracts and a hand search of reference lists and table of contents of relevant journals and books were searched from January 2011 to June 2019. In total, 84 studies met the inclusion criteria. Most studies confirm that low-resourced countries compared with high-resourced countries battle to implement NCDs prevention strategies; fail to record data on the risk factors of NCDs; medical records and surveillance data are unavailable. Due to a lack of knowledge and skill, low-resourced countries show no urgency to implement a systems approach for NCDs management. 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Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:189,paginationItems:[{id:"221831",title:"Prof.",name:"Niansheng",middleName:null,surname:"Tang",slug:"niansheng-tang",fullName:"Niansheng Tang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221831/images/system/221831.jpeg",biography:"Niansheng Tang is a Professor of Statistics and Dean of the School of Mathematics and Statistics, Yunnan University, China. He was elected a Yangtze River Scholars Distinguished Professor in 2013, a member of the International Statistical Institute (ISI) in 2016, a member of the board of the International Chinese Statistical Association (ICSA) in 2018, and a fellow of the Institute of Mathematical Statistics (IMS) in 2021. He received the ICSA Outstanding Service Award in 2018 and the National Science Foundation for Distinguished Young Scholars of China in 2012. He serves as a member of the editorial board of Statistics and Its Interface and Journal of Systems Science and Complexity. He is also a field editor for Communications in Mathematics and Statistics. His research interests include biostatistics, empirical likelihood, missing data analysis, variable selection, high-dimensional data analysis, Bayesian statistics, and data science. He has published more than 190 research papers and authored five books.",institutionString:"Yunnan University",institution:{name:"Yunnan University",country:{name:"China"}}},{id:"1177",title:"Prof.",name:"António",middleName:"J. R.",surname:"José Ribeiro Neves",slug:"antonio-jose-ribeiro-neves",fullName:"António José Ribeiro Neves",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1177/images/system/1177.jpg",biography:"Prof. António J. R. Neves received a Ph.D. in Electrical Engineering from the University of Aveiro, Portugal, in 2007. Since 2002, he has been a researcher at the Institute of Electronics and Informatics Engineering of Aveiro. Since 2007, he has been an assistant professor in the Department of Electronics, Telecommunications, and Informatics, University of Aveiro. He is the director of the undergraduate course on Electrical and Computers Engineering and the vice-director of the master’s degree in Electronics and Telecommunications Engineering. He is an IEEE Senior Member and a member of several other research organizations worldwide. His main research interests are computer vision, intelligent systems, robotics, and image and video processing. He has participated in or coordinated several research projects and received more than thirty-five awards. He has 161 publications to his credit, including books, book chapters, journal articles, and conference papers. He has vast experience as a reviewer of several journals and conferences. As a professor, Dr. Neves has supervised several Ph.D. and master’s students and was involved in more than twenty-five different courses.",institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"11317",title:"Dr.",name:"Francisco",middleName:null,surname:"Javier Gallegos-Funes",slug:"francisco-javier-gallegos-funes",fullName:"Francisco Javier Gallegos-Funes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/11317/images/system/11317.png",biography:"Francisco J. Gallegos-Funes received his Ph.D. in Communications and Electronics from the Instituto Politécnico Nacional de México (National Polytechnic Institute of Mexico) in 2003. He is currently an associate professor in the Escuela Superior de Ingeniería Mecánica y Eléctrica (Mechanical and Electrical Engineering Higher School) at the same institute. His areas of scientific interest are signal and image processing, filtering, steganography, segmentation, pattern recognition, biomedical signal processing, sensors, and real-time applications.",institutionString:"Instituto Politécnico Nacional",institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"428449",title:"Dr.",name:"Ronaldo",middleName:null,surname:"Ferreira",slug:"ronaldo-ferreira",fullName:"Ronaldo Ferreira",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428449/images/21449_n.png",biography:null,institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:null,institution:null},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"417317",title:"Mrs.",name:"Chiedza",middleName:null,surname:"Elvina Mashiri",slug:"chiedza-elvina-mashiri",fullName:"Chiedza Elvina Mashiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"352140",title:"Dr.",name:"Edina",middleName:null,surname:"Chandiwana",slug:"edina-chandiwana",fullName:"Edina Chandiwana",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"342259",title:"B.Sc.",name:"Leonard",middleName:null,surname:"Mushunje",slug:"leonard-mushunje",fullName:"Leonard Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"347042",title:"Mr.",name:"Maxwell",middleName:null,surname:"Mashasha",slug:"maxwell-mashasha",fullName:"Maxwell Mashasha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"2941",title:"Dr.",name:"Alberto J.",middleName:"Jorge",surname:"Rosales-Silva",slug:"alberto-j.-rosales-silva",fullName:"Alberto J. Rosales-Silva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"437913",title:"Dr.",name:"Guillermo",middleName:null,surname:"Urriolagoitia-Sosa",slug:"guillermo-urriolagoitia-sosa",fullName:"Guillermo Urriolagoitia-Sosa",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"435126",title:"Prof.",name:"Joaquim",middleName:null,surname:"José de Castro Ferreira",slug:"joaquim-jose-de-castro-ferreira",fullName:"Joaquim José de Castro Ferreira",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"437899",title:"MSc.",name:"Miguel Angel",middleName:null,surname:"Ángel Castillo-Martínez",slug:"miguel-angel-angel-castillo-martinez",fullName:"Miguel Angel Ángel Castillo-Martínez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"289955",title:"Dr.",name:"Raja",middleName:null,surname:"Kishor Duggirala",slug:"raja-kishor-duggirala",fullName:"Raja Kishor Duggirala",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jawaharlal Nehru Technological University, Hyderabad",country:{name:"India"}}}]}},subseries:{item:{id:"3",type:"subseries",title:"Bacterial Infectious Diseases",keywords:"Antibiotics, Biofilm, Antibiotic Resistance, Host-microbiota Relationship, Treatment, Diagnostic Tools",scope:"
\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
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Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. He is also a reviewer and a member of editorial boards of a number of international journals.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",slug:"katarzyna-garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",biography:"Katarzyna Maria Garbacz, MD, is an Associate Professor at the Medical University of Gdańsk, Poland and she is head of the Department of Oral Microbiology of the Medical University of Gdańsk. She has published more than 50 scientific publications in peer-reviewed journals. She has been a project leader funded by the National Science Centre of Poland. Prof. Garbacz is a microbiologist working on applied and fundamental questions in microbial epidemiology and pathogenesis. Her research interest is in antibiotic resistance, host-pathogen interaction, and therapeutics development for staphylococcal pathogens, mainly Staphylococcus aureus, which causes hospital-acquired infections. Currently, her research is mostly focused on the study of oral pathogens, particularly Staphylococcus spp.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null,series:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188"},editorialBoard:[{id:"190041",title:"Dr.",name:"Jose",middleName:null,surname:"Gutierrez Fernandez",slug:"jose-gutierrez-fernandez",fullName:"Jose Gutierrez Fernandez",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"University of Granada",institutionURL:null,country:{name:"Spain"}}},{id:"156556",title:"Prof.",name:"Maria Teresa",middleName:null,surname:"Mascellino",slug:"maria-teresa-mascellino",fullName:"Maria Teresa Mascellino",profilePictureURL:"https://mts.intechopen.com/storage/users/156556/images/system/156556.jpg",institutionString:"Sapienza University",institution:{name:"Sapienza University of 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\r\n\tThe integration of tissues and organs throughout the mammalian body, as well as the expression, structure, and function of molecular and cellular components, is essential for modern physiology. The following concerns will be addressed in this Cell Physiology subject, which will consider all organ systems (e.g., brain, heart, lung, liver; gut, kidney, eye) and their interactions: (1) Neurodevelopment and Neurodevelopmental Disease (2) Free Radicals (3) Tumor Metastasis (4) Antioxidants (5) Essential Fatty Acids (6) Melatonin and (7) Lipid Peroxidation Products and Aging Physiology.
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The global picture of physiological processes in plants needs to be investigated continually to increase our knowledge, and the resulting technologies will benefit sustainable agriculture.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/13.jpg",keywords:"Plant Nutrition, Plant Hormone, Photosynthesis, Respiration, Plant Stress, Multi-omics, High-throughput Technology, Genome Editing"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 16th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:124,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},subseries:[{id:"3",title:"Bacterial Infectious Diseases",keywords:"Antibiotics, Biofilm, Antibiotic Resistance, Host-microbiota Relationship, Treatment, Diagnostic Tools",scope:"
\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
",annualVolume:11399,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null,editorialBoard:[{id:"190041",title:"Dr.",name:"Jose",middleName:null,surname:"Gutierrez Fernandez",fullName:"Jose Gutierrez Fernandez",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"University of Granada",institutionURL:null,country:{name:"Spain"}}},{id:"156556",title:"Prof.",name:"Maria Teresa",middleName:null,surname:"Mascellino",fullName:"Maria Teresa Mascellino",profilePictureURL:"https://mts.intechopen.com/storage/users/156556/images/system/156556.jpg",institutionString:"Sapienza University",institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"164933",title:"Prof.",name:"Mónica Alexandra",middleName:null,surname:"Sousa Oleastro",fullName:"Mónica Alexandra Sousa Oleastro",profilePictureURL:"https://mts.intechopen.com/storage/users/164933/images/system/164933.jpeg",institutionString:"National Institute of Health Dr Ricardo Jorge",institution:{name:"National Institute of Health Dr. Ricardo Jorge",institutionURL:null,country:{name:"Portugal"}}}]},{id:"4",title:"Fungal Infectious Diseases",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",annualVolume:11400,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"302145",title:"Dr.",name:"Felix",middleName:null,surname:"Bongomin",fullName:"Felix Bongomin",profilePictureURL:"https://mts.intechopen.com/storage/users/302145/images/system/302145.jpg",institutionString:null,institution:{name:"Gulu University",institutionURL:null,country:{name:"Uganda"}}},{id:"45803",title:"Ph.D.",name:"Payam",middleName:null,surname:"Behzadi",fullName:"Payam Behzadi",profilePictureURL:"https://mts.intechopen.com/storage/users/45803/images/system/45803.jpg",institutionString:"Islamic Azad University, Tehran",institution:{name:"Islamic Azad University, Tehran",institutionURL:null,country:{name:"Iran"}}}]},{id:"5",title:"Parasitic Infectious Diseases",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",annualVolume:11401,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188881",title:"Dr.",name:"Fernando José",middleName:null,surname:"Andrade-Narváez",fullName:"Fernando José Andrade-Narváez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRIV7QAO/Profile_Picture_1628834308121",institutionString:null,institution:{name:"Autonomous University of Yucatán",institutionURL:null,country:{name:"Mexico"}}},{id:"269120",title:"Dr.",name:"Rajeev",middleName:"K.",surname:"Tyagi",fullName:"Rajeev Tyagi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRaBqQAK/Profile_Picture_1644331884726",institutionString:"CSIR - Institute of Microbial Technology, India",institution:null},{id:"336849",title:"Prof.",name:"Ricardo",middleName:null,surname:"Izurieta",fullName:"Ricardo Izurieta",profilePictureURL:"https://mts.intechopen.com/storage/users/293169/images/system/293169.png",institutionString:null,institution:{name:"University of South Florida",institutionURL:null,country:{name:"United States of America"}}}]},{id:"6",title:"Viral Infectious Diseases",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",annualVolume:11402,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",fullName:"Emmanuel Drouet",profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",institutionString:null,institution:{name:"Grenoble Alpes University",institutionURL:null,country:{name:"France"}}},{id:"188219",title:"Prof.",name:"Imran",middleName:null,surname:"Shahid",fullName:"Imran Shahid",profilePictureURL:"https://mts.intechopen.com/storage/users/188219/images/system/188219.jpeg",institutionString:null,institution:{name:"Umm al-Qura University",institutionURL:null,country:{name:"Saudi Arabia"}}},{id:"214235",title:"Dr.",name:"Lynn",middleName:"S.",surname:"Zijenah",fullName:"Lynn Zijenah",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSEJGQA4/Profile_Picture_1636699126852",institutionString:null,institution:{name:"University of Zimbabwe",institutionURL:null,country:{name:"Zimbabwe"}}},{id:"178641",title:"Dr.",name:"Samuel Ikwaras",middleName:null,surname:"Okware",fullName:"Samuel Ikwaras Okware",profilePictureURL:"https://mts.intechopen.com/storage/users/178641/images/system/178641.jpg",institutionString:null,institution:{name:"Uganda Christian University",institutionURL:null,country:{name:"Uganda"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/317075",hash:"",query:{},params:{id:"317075"},fullPath:"/profiles/317075",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()