Examples of HEPs given in [52].
\r\n\tMany tried to define it, and its definition is always related to those who are in power, that being explained by the fact that this power and the abuse of it precisely, gives the access to being corrupted and practicing the acts that fall under corruption.
\r\n\r\n\tWe can find various types of corruption such as bribery, lobbying, extortion, cronyism, nepotism, parochialism, patronage, influence peddling, graft, and embezzlement. Also giving or accepting bribes or inappropriate gifts, double-dealing, under-the-table transactions, manipulating elections, diverting funds, laundering money, and defrauding investors.
\r\n\tNo government is immune to corruption. According to the World Bank, “the causes of corruption are always contextual, rooted in a country's policies, bureaucratic traditions, political development, and social history”.
\r\n\tThis indeed has consequences for increasing inequality, impacts government expenditure and services, shadow economy, and crime.
\r\n\tThis book will be a collection of chapters on Corruption. It welcomes contributions related to the nature of corruption its types and how corruption is undertaken in a certain context and the ways to deal with corruption will be part of this book. We value including materials on Corruption in organizations and ways to solve it. The origins of corruption and the way to deal with corruption, how to provide solutions, and any new insights on corruption will be part of this book.
",isbn:"978-1-80356-696-2",printIsbn:"978-1-80356-695-5",pdfIsbn:"978-1-80356-697-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"9cda6d2feaa52a6d523da74f2e2d7ffb",bookSignature:"Dr. Josiane Fahed-Sreih",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11772.jpg",keywords:"Corruption, Origins, Types, Corporate Governance, Organizational Performance, Solutions, Corruption Index, Private Sector, Lebanon, Accountability, Anti-corruption, Public Policy",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 23rd 2022",dateEndSecondStepPublish:"April 20th 2022",dateEndThirdStepPublish:"June 19th 2022",dateEndFourthStepPublish:"September 7th 2022",dateEndFifthStepPublish:"November 6th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Fahed-Sreih is the director of the Institute of Family and Entrepreneurial Business and a chairperson in the Department of Management. 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She is the founder and director of the Institute of Family and Entrepreneurial Business and a chairperson in the Department of Management at the same university. She was previously the assistant dean. She obtained a Ph.D. from Sorbonne University, Paris, France. Dr. Fahed-Sreih is the Middle East Coordinator for the Family Firm Institute (FFI), the USA, and a family wealth and family business consultant. She received the 2007 FFI International Award for outstanding achievement in furthering the understanding of family business issues that occur between two or more countries. She has participated in and organized international conferences, workshops, and seminars. She has presented at major conferences locally and internationally and consulted on management issues in many countries, including Saudi Arabia, Dubai, Jordan, Qatar, Kuwait, Syria, Bahrain, Oman, France, Cyprus, and Lebanon. She currently sits on five boards of directors as a shareholder, two as a chairman of the board, and one as an independent director in the private sector. She is also an advisor on boards of community service organizations. \n\nShe speaks regularly to trade and professional groups and presents her research at academic conferences worldwide. She is frequently invited as a keynote speaker to the recognized family business and corporate governance conferences. Her research interests are in management, family business, the functioning of boards of directors, and corporate governance. She has published three books, several book chapters, and academic articles in international journals. 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While extremely resourceful and capable of dealing with unexpected circumstances, humans are also prone to errors. Although significant technological, organizational, operational, and other advances have been made in recent decades, catastrophic accidents driven by human errors are still a regular, albeit increasingly rare, occurrence. Recently, the realization that complete elimination of all human errors will probably never be achievable has took hold [1]. As with any system that requires high degree of safety, ATM system solves this issue by employing multiple levels of risk and safety management, each providing a layer of the safety net. Nevertheless, methods for reducing the human error are still widely used and being researched. These methods, which consider the effect of human error on risk and reliability, are generally classified under the name of human reliability analysis (HRA). This chapter explores the applicability of HRA as part of the overall risk assessment with a focus on air traffic complexity issues.
There are many motivations for performing a risk or reliability analysis. In most cases it is to reduce the potential for system failure caused by humans. In this case the risk analysis can be used either in the design process or during the operation. Sometimes it is needed to change or restructure the organizational design in a manner which ensures at least the same level of safety as before. In other cases, risk analysis can be performed as a part of licensing arrangements where the operator is tasked with assuring that a system meets a safety target. Or it can be used during the decision-making process where an operator chooses one of the possible systems to procure. In many of these cases, HRA will be undertaken as part of the more comprehensive risk assessment process.
Air traffic controllers (ATCOs) are at the core of the ATM. They are the central node where most important safety-related tactical decisions are made. Their job is to gather information and process them with the goal of reaching solutions which ensure safe and cost-efficient air traffic. One of their main tasks is prioritization of actions because human mental capacity is limited and it has been shown that ATCOs frequently deal with information overload [2]. Previous research showed that overload usually causes performance decline [3]. Air traffic complexity is one of the main factors driving the increase in the ATCO workload, so it is a reasonable assumption that increased complexity will result in increased errors due to decay in ATCO performance. Therefore, it is important to be able to assess air traffic complexity as a possible source factor of risk.
In this chapter, the connection between air traffic complexity, controller workload, HRA, and risk assessment will be made. For that purpose, in Section 2, a brief overview of complexity will be made, starting with definition and ending with assessment methods. In Section 3, a broader area of decision complexity and inherent difficulty of making the correct decision in a complex system will be presented. In Section 4, a very brief overview of HRA methods relevant to ATM system will be presented, as well as an HRA method developed specifically for use in ATM. In the latter parts of Section 4, an example of how to include the air traffic complexity into the HRA will be shown, and, in comparison, risk analysis based on real-time human-in-the-loop (HITL) simulations will be presented.
The Random House dictionary defines complexity as “the state or quality of being complex; intricacy”, and complex as “composed of many interconnected parts; compound; composite
On the other hand, Cilliers, in his seminal book on the topic, claims exactly the opposite [6]:
One example of such thinking is presented by Snowden in [7]. He claims that the aircraft can be considered complicated due to many parts. Once disassembled and analyzed, the function of all parts and their relationships can be determined. Human organizations and systems are, on the other hand, complex. They are made up of many interacting agents, with agent being any component of the system with identity. Agents can have multiple identities based on the context, i.e., a person can assume group identity or switch between formal and informal identities based on the environment. As these identities change, the components of the system change, the rules an agent follows change, and interactions between the components change. This makes it impossible to distinguish between the cause and effect because they are intertwined [8].
In the context of air traffic control, complexity was rarely clearly defined, perhaps due to assumed common knowledge. One notable exception is Meckiff (et al.) who stated that the air traffic complexity can be most easily defined as
Complexity is
Complexity is
The relationship between air traffic complexity and workload.
Controller cognitive strategies can be improved through training and experience that is readily seen when comparing experienced and inexperienced controllers. However, if one takes into consideration an average controller with average training, only two avenues to reduced controller workload remain—increasing equipment quality and decreasing complexity.
Complexity was a common research topic since the early days of modern ATC operations. First papers that mention complexity were written in the early 1960s [16]. Since then, dozens of papers and reports were written on the topic of complexity—excellent reviews of those papers were written by Mogford [11] and Hilburn [17]. Instead of writing a completely new literature review, this chapter will present important research paths, ideas, methods, and facts, which are relevant to the present research.
It needs to be noted that most of the early research was conducted in order to better define factors that affect workload. Today, most of those factors, with present understanding and definitions, would probably be called complexity factors. Some studies were nonempirical and lack exact definitions and measurement methods for complexity indicators. Those studies were excluded from this short review to give more room to those studies with experimentally validated complexity factors.
Schmidt [18] approached the problem of modelling controller workload from the angle of observable controller actions. He created the control difficulty index, which can be calculated as a weighted sum of the expected frequency of occurrence of events that affect controller workload. Each event is given a different weight according to the time needed to execute a particular task. Though the author conducted extensive surveys to determine appropriate weights and frequencies for various events, this approach can only handle observable controller actions, which makes it very limiting.
Hurst and Rose [19], while not the first to realize the importance of traffic density, were first to measure the correlation of expert workload ratings with traffic density. They concluded that only 53% of the variance in reported workload ratings can be explained by density.
Stein [20] used Air Traffic Workload Input Technique (ATWIT), in which controllers report workload levels during simulation, to determine which of the workload factors influenced workload the most. Regression analysis proved that out of the five starting factors, four factors (localized traffic density, number of handoffs outbound, total amount of traffic, number of handoffs inbound) could explain 67% of variance in ATWIT scores. This study showed the importance of localized traffic density which is a measure of traffic clustering. Technique similar to ATWIT will be used throughout the next three decades, including a modified ATWIT scores that will be used in this research.
Laudeman et al. [21] expanded on the notion of the traffic density by introducing dynamic density which they defined as a combination of “both traffic density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace).” Authors used informal interviews with controllers to obtain a list of eight complexity factors to be used in dynamic density equation. The only criterion was that the factors could be calculated from the radar tracks or their extrapolations. The intention was to obtain an objective measure of controller workload based on the actual traffic. Their results showed that the dynamic density was able to account for 55% of controller activity variation. Three other teams [13, 22, 23] working under the Dynamic Density program developed additional 35 complexity indicators (factors), which were later successfully validated as a group by Kopardekar et al. [24]. Unfortunately, it was later shown that the complexity indicator weights were not universal to all airspace sectors, i.e., they had to be adjusted on a sector-by-sector basis [25]. This shortcoming, while making the dynamic density technique difficult to implement for operational purposes, has no influence if one wishes to compare two concepts of operations under similar conditions (similar sector configuration). Furthermore, same authors [24] suggested that, due to possibly nonlinear interactions between complexity factors, the dynamic density performance could be improved by using nonlinear techniques such as nonlinear regression, genetic algorithms, and neural networks.
Almost the same group of authors will use multiple linear regression method 5 years later to determine which subset of complexity indicators will correlate well with the controller’s subjective complexity ratings [26]. After extensive simulator validation, results of this study showed that there are 17 complexity indicators that are statistically significant. Top five complexity indicators were sector count, sector volume, number of aircraft under 8 NM from each other, convergence angle, and standard deviation of ground speed/mean ground speed. Similar work was done by Masalonis et al. [27] who selected a subset of 12 indicators and Klein et al. [28] who selected a subset of only 7 complexity indicators, though with less extensive experimental validation.
In a similar vein, Bloem et al. [29] tried to determine which of the complexity indicators had the greatest predictive power in terms of future complexity. The authors concluded that there is a significant difference in predictive power of different complexity indicators. To complicate the matter further, they concluded that the subset of the complexity indicators that had the best predictive power changed depending on the prediction horizon.
To calculate potential impact of air traffic complexity on workload and costs, in 2000 the EUROCONTROL has given the same set of traffic data to UK National Air Traffic Services (NATS) and the EUROCONTROL Experimental Centre (EEC) with a task of independently devising a method of measuring the level of service [30]. While NATS has estimated ATS output (the service provided), the EEC has estimated the ATS workload needed to deliver the service. Both “were found to produce reasonably consistent results
First to consider measuring complexity during TBO were Prevot and Lee [32]. They coined the term trajectory-based complexity (TBX) which is a measure of complexity in TBO. The basis of the TBX calculation is a set of nominal conditions—nominal sector size, nominal number of transitioning aircraft, and a nominal equipage mix. Any difference to nominal operations causes a modification to the TBX value. Authors do not explain the method to determine the nominal conditions except that they can “be defined through knowledge elicitation sessions on a sector by sector basis or based upon more generic attributes.” The TBX value is then a number of aircraft that would produce the same workload under the nominal conditions as do aircraft under real conditions (e.g., the TBX of 20 means that the workload is equal to the aircraft count of 20 under nominal conditions even though there are actually only 16 aircraft in the sector). The advantage of this method is that it gives a single complexity value that can be easily related to aircraft count and is thus very user-friendly and self-explanatory (unlike many other complexity metrics). However, this study included only six complexity indicators with weights that were determined in an ad hoc manner and hardly any validation with actual subjective complexity. Only one of those complexity indicators was indirectly related to TBO (number of aircraft with data-link). Many human-in-the-loop simulation runs were performed in which the controllers had to give workload scores which were then compared with TBX value and simple aircraft count. While the authors claim that the subjective workload score correlated better with the TBX value, there was no objective correlation assessment presented. Finally, the authors have not compared the effect of fraction of TBO aircraft on air traffic complexity.
In a subsequent paper by same authors, the relationship between workload and data-link equipage levels was explored [33]. It was concluded that the workload ratings correlated much better with the TBX score than with the aircraft count for varying data-link equipage levels.
Prandini et al. have developed a new method of mapping complexity based exclusively on traffic density [34]. This method is applicable only to the future concept of aircraft self-separation and does not take into account the human factors at all.
Gianazza [35, 36, 37] proposed a method for prediction of air traffic complexity using tree search methods and neural networks. This method is based on the assumption that the air traffic complexity in historic flight data increased prior to the splitting of the collapsed sector into two smaller ones and decreased prior to collapsing the sectors into a larger one. The neural network was trained using this historical data, and then it could predict future increase in air traffic complexity. Tree search method was then used to determine the airspace configuration which yields lowest workload and complexity for the given air traffic pattern.
Lee et al. [38] have proposed that airspace complexity can be described in terms of how the airspace (together with the traffic inside it and the traffic control method) responds to disturbances. The effect of disturbances on control activity needed to accommodate that disturbance is what defines complexity in their opinion. The more control activity needed, the more complex the airspace is. They propose a tool, airspace complexity map, which should help to plan the airspace configuration and the future development of ATM.
In Radišić et al. [39], authors used domain-expert assessment to test the effect of the trajectory-based operations (TBO) on air traffic complexity. ATCOs were recruited to perform human-in-the-loop (HITL) simulations during which they were asked to provide real-time assessment of air traffic complexity. Linear regression model was used to select, among 20 most used complexity indicators, those indicators which correlated best with subjective complexity scores. Six indicators were used to generate a predictive linear model that performed well in conventional operations but less so under TBO. Therefore, the authors defined and experimentally validated two novel TBO-specific complexity indicators. A second correlation model combining these two novel indicators with four already in use generated much better predictions of complexity than the first model. Nonetheless, the best correlation that was achieved was R = 0.83 (R2-adjusted = 0.691). In subsequent work, the authors attempted to achieve better prediction by using artificial neural networks; however, similar results were obtained. This indicates that there is some variation in subjective complexity scores provided by ATCOs that cannot be explained by traffic properties. Indeed, it might be the case that ATCOs introduce a degree of noise into the complexity scores due to difficulty of maintaining the consistent scoring criteria [40].
Wang et al. [41] in their work used network approach to calculate air traffic complexity based on historical radar data. Their assumption is that air traffic situation is essentially a time-evolving complex system. In that system aircraft are key waypoints; route segments are nodes; aircraft-aircraft, aircraft-keypoint, and aircraft-segment complexity relationships are edges; and the intensities of various complexity relationships are weights. The system was built using a dynamic weighted network model.
Xue et al. [42] in their work analyzed three complexity indicators for simulated UAS traffic: number of potential conflicts, scenario complexity metric, and number of flights. Scenario complexity metric is based on cost of pairwise conflict which is defined as deviation from the original path. To perform analysis on around 1000 scenarios at different density levels, authors had to develop a UAS simulator. Analysis was done using Pearson and ACE statistics methods.
Future concept of operations will involve usage of far wider range of air traffic controller tools; therefore, it is expected that new complexity indicators related to interaction of controllers and equipment will have to be developed. Furthermore, novel complexity assessment methods are needed due to limits of current techniques.
In this section, several air traffic complexity estimation methods will be examined in greater detail. All complexity estimation methods are based on the traffic data which describes a traffic situation. Since the complexity is a psychological construct, the most relevant estimator of complexity in a given traffic situation is the air traffic controller. The air traffic controller can look at the traffic data and decide whether a traffic situation is complex or not. All other methods are just attempts at approximating the level of complexity as estimated by the controller. The main problem with expert-based estimation is the inconsistency between controllers, where one controller gives a different complexity estimate than the other. Therefore, most other methods seek ways to make the complexity estimate without human input. Ideally, those other methods would be validated by comparing them to the expert, i.e., controller’s estimate; however, this is not always the case.
Three main methods of control-based (i.e., based on ATCOs’ experience of complexity as a driver for workload and, subsequently, limiting factor of airspace capacity) air traffic complexity estimation will be presented here:
Expert-based air traffic complexity estimation—where an expert, in most cases an air traffic controller, gives their estimate of the complexity
Indicator-based air traffic complexity estimation—where the values of complexity indicators, derived from traffic data, are used to determine the level of complexity
Interaction-based air traffic complexity estimation—where the complexity is estimated on the basis of the number of aircraft interactions in a given airspace cell (this method could be broadly defined as a very narrow indicator-based complexity estimation method due to a very low number of indicators)
Others—methods based on other principles, such as counting the number of clearances [43], evaluating proximity based on probabilistic occupancy of airspace [44], measuring sensitivity to initial conditions of the underlying dynamic system called Lyapunov exponents (i.e., assessing predictability of traffic) [45], and many others
The decision-making process needs to be adapted to the context in which the operations take place. It is often seen that one kind of decision-making, completely adapted to its environment and therefore useful, cannot be easily transferred to another environment. This is often the case with accomplished engineers being notably less successful after moving into the managerial role.
Classification of such environments and appropriate decision-making modes is sometimes attempted with the goal of making rules about the best ways to manage each context. However, this is not an exact science because there are multiple factors that can change the decision-making context depending on who the person making the decision is, or how experienced they are. Nevertheless, there is still utility in being aware of the environment in terms of decision contexts and learning how to detect when the environment shifts from one domain to another.
One such classification attempt is the
Simple (also, obvious)—In this domain the situation is well known and stable. The cause–effect relations are established and rarely change. Following procedures and best practices is the best course of action to ensure efficient realization of goals. Decision-making process is usually made of the sense-categorize-respond steps. A major issue in this domain is the overreliance on patterns and routine behavior which stifles innovation and precludes any change. This has caused many issues in the past when organizations were not willing to adapt to changes or innovate, but on the other hand, this has also created many opportunities for disruption by newcomers.
Knowable (also, complicated)—This domain includes environments in which not everything is known but everything can be understood with enough time and effort. In
Complex—In this domain are environments or systems which cannot be analyzed by breaking them down into smaller pieces, analyzing them individually, and creating the big picture based on the analysis of individual components. The very act of interacting with the system introduces changes which cannot always be predicted. The main mode of management of complex systems is through observation of patterns, finding ways to sustain those patterns we desire, and disrupting those we do not. One particular phenomenon that arises in complex systems is the so-called retrospective coherence. The state of the system seems logical and coherent once it is retrospectively analyzed; however, current state of the system could hardly be anticipated in advance because there are many other equally plausible system states.
Chaotic—Chaotic systems cannot be analyzed for cause and effect relationships. Patterns are not visible, and if one waits for patterns to emerge, the damage could become disastrous. It is in these conditions that the system is most difficult to manage but also most capable of change, for better or for worse.
Air traffic control is all about making decisions, so it is not a novel idea to apply the Cynefin framework to the ATC operations even though Cynefin was originally proposed for business-related decision-making [46]. Air traffic control is a complex system with numerous human and machine agents, organized in deep layers of components glued by multiple communication modes and protocols. This is even more apparent in air traffic management systems. Although someone might look at the routine ATC operations and consider them simple, or even mechanistic, such thinking is a sure way towards probably costly failure. In our opinion, ATC operations can be assigned to all domains depending on the traffic situation or changes in states of the system:
During the nominal low-traffic ATC operations, the traffic situation is easy enough in terms of workload to be considered as belonging to the
In nominal high traffic ATC operations, the number of interactions rises and so does the difficulty of maintaining safe and efficient air traffic. The situation needs to be sensed and then analyzed for all the tasks that need to be performed. Tasks are often prioritized based on the urgency and difficulty. A lot more time is spent on this analysis than in low-traffic situation. The ATCO then solves the issues by applying solutions that are considered to be best practice. There are multiple ways of solving an issue, and all of them are correct if the safety is maintained and flight efficiency is not unreasonably reduced. Unless there is some source of major uncertainty present, such as adverse weather conditions, this type of operations is best described as belonging to the
In off-nominal operations of any traffic level or nominal operations with a major source of uncertainty, such as adverse weather, the decision context often enters the
Operations in the
It should be noted here that air traffic complexity should not be confused with
The main purpose of this classification of decision contexts in ATC is to help make ATCOs and supervisors aware of the different environments that are possible behind the seemingly unchanging radar screen. Another purpose, which will be discussed in the next section of this chapter, is to lay down the framework for assessing risks associated with air traffic complexity.
Complexity in ATM is often split into two parts: airspace complexity (static complexity) and air traffic complexity (dynamic complexity). It is generally agreed that both dynamic and static components of complexity can affect controller workload and influence the probability of occurrence of an ATC (i.e., controller) error. Dynamic complexity relates to the factors describing air traffic complexity, i.e., it can include factors such as traffic volume, climbing/descending traffic, mix of aircraft type, military area activity, and types of aircraft intersection. Static factors, on the other hand, encompass factors related to the airspace, such as airspace structure, proximity of reporting points to sector boundaries, and standing agreements between ANSPs.
In a human factors study, areas rated as some of the biggest contributors to risk in ATM are workload, human error, allocation of function, and situational awareness [48]. As mentioned previously, air traffic complexity is a measure of difficulty of controlling the air traffic in a given sector; therefore, it is a direct contributor to workload. In a sense, ATCO’s job is to make correct decisions, whereas air traffic complexity is a factor that makes the search for the right decision more difficult. Therefore, increased complexity can directly increase the probability of a wrong decision being made because the size of the search space increases faster than the set of correct solutions. Here lies the main connection between air traffic complexity and risk. Probability of human error (i.e., human error risk) increases with increased complexity. Thus, it is reasonable to assess the complexity-related risks from the human reliability assessment point of view.
EUROCONTROL investigated the possible relationship between ATM system complexity and safety. They tried to develop a complexity hazard and operability (HAZOP) technique with the main objective being to trial this approach and evaluate its utility for safety assessment and obtain feedback on its acceptability with operations personnel [49]. The attempt at developing complexity HAZOP was unsuccessful due to difficulty of adjusting the HAZOP technique to the complexity issues. Therefore, in this section only HRA methods will be presented.
This section will provide a brief overview of the HRA methods and their development over the years; however, for a more thorough review of HRA methods, one can find more information in [46, 50]. Only those methods that are in some way relevant to HRA in aviation will be considered.
HRA can be defined as “any method by which human reliability is estimated” [51], and it is generally presented as having three main parts: (1) identifying possible human errors and contributors, (2) modelling human error, and (3) quantifying human error probabilities. These methods were first developed in nuclear power safety systems.
In the early models of HRA, human was often considered as just another part of the system. For example, in [52], a
Error | HEP |
---|---|
Failure to perform rule-based actions correctly when written procedures are available and used (with recovery) | 0.025 |
Inadvertent activation of a control; select wrong control on a panel from an array of similar-appearing controls identified by labels only | 0.003 |
Omitting a step or important instruction from a formal or ad hoc procedure | 0.003 |
Omitting an item of instruction when use of written procedures is specified (<10 items) | 0.001 |
Checking the status of equipment if that status affects one’s safety when performing his tasks | 0.001 |
Turn rotary control in the wrong direction when there is no violation of populational stereotypes | 0.0005 |
Errors of commission in check-reading analog meters with easily seen limit marks | 0.001 |
Examples of HEPs given in [52].
THERP also specified performance shaping factors (PSF) which were used to modify the nominal HEPs based on the context of the action (e.g., time pressure, human-machine interface, etc.). A list of possible PSFs for one error is given in Table 2. One can notice that there are no error multipliers associated with each PSF. It is the duty of the assessor to define the maximum affect that each PSF could have on HEPs. Criticism of THERP was mostly that it was too difficult to apply because of quite detailed decomposition of tasks that it relied on a database of HEPs which was never really validated and that it took very broad and casual definitions of human performance factors.
1 | Stress level of the operator |
2 | Rate at which the operator must process signals |
3 | Frequency with which a particular display is scanned |
4 | Whether a written checklist is used to direct the operator to specific displays |
5 | Relationship of the displays to annunciators or other attention-getting devices |
6 | Extent to which the information needed for operator decisions and actions is displayed directly |
7 | Human factors engineering related to the design and arrangement of the displays |
Examples of PSFs for errors related to reading unannunciated displays [52].
Another version of this type of model was done in human error assessment and reduction technique (HEART) [53]. The database of HEPs was much smaller and more generic, so it was more flexible and easier to apply than THERP. Instead of highly detailed errors, the focus is on a handful of generic task types for which probabilities of failure are given (Table 3). This simplification has made the HEART technique much more accepted outside the nuclear power industry for which the THERP was designed.
Generic task | Proposed nominal human unreliability | 5th–95th percentile bounds |
---|---|---|
Totally unfamiliar, performed at speed with no real idea of likely consequences | 0.55 | 0.35–0.97 |
Shift or restore system to a new or original state on a single attempt without supervision or procedures | 0.26 | 0.14–0.42 |
Complex task requiring high level of comprehension and skill | 0.16 | 0.12–0.28 |
Fairly simple task performed rapidly or given scant attention | 0.09 | 0.06–0.13 |
Routine, highly practiced, rapid task involving relatively low level of skill | 0.02 | 0.007–0.045 |
Completely familiar, well-designed, highly practiced, routine task occurring several times per hour, performed to highest possible standards by highly motivated, highly trained, and experienced person, totally aware of implications of failure, with time to correct potential error but without the benefit of significant job aids | 0.0004 | 0.00008–0.009 |
Respond correctly to system command even when there is an augmented or automated supervisory system providing accurate interpretation of system state | 0.00002 | 0.000006–0.0009 |
Generic tasks and proposed human unreliability in HEART technique [53].
The author has identified the human factors he found relevant by searching the human factors literature and assigned relative weights to them, identified impacts of errors, and suggested a set of human error data which should enable higher reliability of the system. Instead of calling them PSFs, the author called them error-producing condition (EPC) and provided the multipliers for each. Multipliers are used to increase the nominal human unreliability in cases where there are circumstances that increase the probability of human error. Some of the EPCs are shown in Table 4.
Error-producing condition | Maximum predicted increase in unreliability when going from good conditions to bad |
---|---|
Unfamiliarity with a situation which is potentially important but which only occurs infrequently or which is novel | ×17 |
A shortage of time available for error detection and correction | ×11 |
A low signal-to-noise ratio | ×10 |
A means of suppressing or overriding information or features which is too easily accessible | ×9 |
No means of conveying spatial and functional information to operators in a form which they can readily assimilate | ×8 |
A mismatch between an operator’s model of the world and that imagined by a designer | ×8 |
EPCs and their multipliers as proposed in the HEART technique [53].
More generic error types have led to confusion when trying to apply it to a specific industrial application. This problem was addressed by developing specialized versions of HEART for specific industries. One such derivative will be discussed shortly.
These models are characterized by defining two broad categories of errors: errors of omission (when human operator fails to make an action) and errors of commission (when operator makes a wrong action). These simplifications were later put, at least partially, into the context of actual human behavior which knows many other ways of committing an error. For example, [54, 55] included contextual effect such as stress, organizational culture, and tiredness into the model, whereas [56, 57] also included the possible variation in operator’s responses and recovery actions undertaken once the errors have been noticed. By taking into account the context of human behavior, these techniques have made a qualitative step forward in comparison to the THERP and HEART, so they are generally called second-generation HRA techniques. This did not, however, improve their adoption in the industry because simpler and more flexible techniques, such as HEART, are more usable and sustainable. For this reason, the first HRA technique developed specifically for ATM was based on HEART technique. It was developed in 2008 and named Controller Action Reliability Assessment (CARA) [58].
Compared to HEART, CARA’s generic task types were developed to better suit the needs of HRA in ATM (Table 5). To make sure that the task types are in line with the commonly used models of ATCO tasks, the basis for task development was found in EUROCONTROL’s studies. Literature and ergonomics database reviews were undertaken to find the data which supports new values of HEPs for each generic task type. Where more than one error probability for a given task was found in the literature or the databases, geometric mean was used to establish a single value. Furthermore, uncertainty bounds of each HEP were determined using the single sample t-test [59].
Task context | Generic task type | HEP | Uncertainty bounds |
---|---|---|---|
A. Offline tasks | A. Offline tasks | 0.03 | — |
B. Checking | B1. Active search of radar or FPS, assuming some confusable information on display | 0.005 | 0.002–0.02 |
B2. Respond to visual change in display (e.g., aircraft highlighted changes to low-lighted) | 0.13 | 0.05–0.3 | |
B3. Respond to unique and trusted audible and visual indication | 0.0004 | — | |
C. Monitoring for conflicts or unanticipated changes | C1. Identify routine conflict | 0.01 | Holding value’ |
C2. Identify unanticipated change in radar display (e.g., change in digital flight level due to aircraft deviation or corruption of datablock) | 0.3 | 0.2–0.5 | |
D. Solving conflicts | D1. Solve conflict which includes some complexity. Note for very simple conflict resolution consider use of GTT F | 0.01 | Holding value’ |
D2. Complex and time pressured conflict solution (do not use time pressure EPC) | 0.19 | 0.09–0.39 | |
E. Plan aircraft in/out of sector | E. Plan aircraft in/out of sector | 0.01 | Holding value’ |
F. Manage routine traffic | F. Routine element of sector management (e.g., rule-based selection of routine plan for an aircraft or omission of clearance) | 0.003 | Holding value’ |
G. Issuing instructions | G1. Verbal slips | 0.002 | 0.001–0.003 |
G2. Physical slips (two simple choices) | 0.002 | 0.0008–0.004 |
Generic task types used in CARA technique [59].
Holding values are to be updated once more data is available.
EPCs used in CARA were, like general task types, developed by adjusting EPCs from HEART and other techniques (most notably SPAR-H [60] and CREAM [61]). To ensure that the CARA EPCs closely follow the well-established contextual structure used in ATC, they were modelled to fit the Human Error in ATM (HERA) [62] classification structure. For initial consideration, CARA EPCs’ maximum affect values were taken from HEART, SPAR-H, and CREAM by selecting the most similar EPCs and then picking the one with the highest value (Table 6). It is expected that with further refinement of underlying data, the maximum affect values will be adjusted to better suit the actual values in ATC.
HERA element | CARA EPCs | Maximum affect |
---|---|---|
Documentation/ procedures | 1. Shortfalls in the quality of information conveyed by procedures | 5 |
Training and experience | 2. Unfamiliarity and adequacy of training/experience | 20 |
3. On-the-job training | 8 | |
Workplace design/HMI | 4. A need to unlearn a technique and apply one which requires the application of an opposing philosophy—stereotype violation | 24 |
5. Time pressure due to inadequate time to complete the task | 11 | |
6. Cognitive overload, particularly one caused by simultaneous presentation of non-redundant information | 6 | |
7. Poor, ambiguous, or ill-matched system feedback—general adequacy of the human-machine interface | 5 | |
8. Trust in system | — | |
9. Little or no independent checking | 3 | |
10. Unreliable instrumentation | 1.6 | |
Environment | 11. Environment—controller workplace noise/lighting issues, cockpit smoke | 8 |
Personal factor issues | 12. High emotional stress and effects of ill health | 5 |
13. Low vigilance | 3 | |
Team factor issues | 14. Difficulties caused by team coordination problems or friction between team members | 10 |
15. Difficulties caused by poor shift hand-over practices | 10 | |
Pilot-controller communication | 16. Communications quality | — |
Traffic and airspace issues | 17. Traffic complexity | 10 |
18. Unavailable equipment/degraded mode—weather issues | — | |
Weather | 19. Weather | — |
Non-HERA: organizational culture | 20. Low workforce morale or adverse organizational environment | 2 |
Non-HERA: cognitive style | 21. Shift from anticipatory to reactive mode | 10 |
22. Risk taking | 4 |
CARA EPCs and values of their maximum affect [59].
For the first time here, one can see that the traffic complexity was taken into account (EPC 17) with maximum affect of 10. CARA User’s Manual provides additional information about this EPC, adding three anchor points for this EPC [63]:
Higher than normal traffic levels with some non-routine conflicts to solve (EPC multiplier 0.1)
Higher than normal traffic levels with some non-routine conflicts requiring constrained solutions; possibility of secondary conflicts (conflict resolution can lead to a second conflict) (EPC multiplier 0.5)
High traffic levels with unusual patterns of traffic requiring problem solving and a number of future conflicts requiring resolution (EPC multiplier 1.0)
EPC multipliers are used to scale the EPC affect from its maximum value to the actual value for the situation that is being assessed, thus getting the actual effect (AE). As is the case with many HRA techniques, some expert opinion is needed here to determine where the assessed scenario falls on the scale of 0.1–1.0. An example of human error risk calculation is given in the next section.
To better show how CARA is used to assess the effect of complexity on ATCO error risk, a simple example will be used. In this example, we suppose that the ATCO is working on an en route sector with moderately high air traffic complexity. Weather is calm and there are no failures in any of the air or ground equipment. In these conditions, we might want to assess the probability that the ATCO will not notice a conflict.
To do this, we select a generic task type (GTT) that best suits our situation. Here, it is
The result shows that the probability of ATCO failing to notice a conflict in a moderately complex situation is 0.046 or 4.6%. The −1 and +1 in Eq. 1 are added to ensure that the resulting EPC is more than 1 without needlessly increasing the EPC (e.g., if only the final +1 was added). Conversely, the probability of ATCO identifying a conflict is equal to 95.4%. These probabilities are valid for a situation with only one ATCO; however, en route ATC operations are usually performed with two ATCOs handling a sector (planning and executive ATCOs). The probability that both ATCOs will fail to notice the conflict is equal to 0.046 x 0.046 = 0.0021 which is to say that approximately 1 in 500 conflicts in moderately complex traffic situations will not be identified (step 1 in Figure 2). Fortunately, ATC tools, such as short-term conflict alert (STCA), will sound the alarm in that case, and the ATCO will have the opportunity for a timely recovery.
Probability tree for conflict resolution in moderately complex traffic according to CARA.
This calculation showed how to use CARA to determine probability of a single event. Events can be chained into probability trees to calculate the probability of a sequence of events. Building on the previous example, we can calculate the probabilities of further events after the conflict was identified or after a conflict was missed. First possibility, and a more probable one, is that the conflict was identified. Next step for ATCOs is to solve it. Let us assume that this task can be assigned to the
If the conflict was missed or the ATCO could not solve it in time, STCA will sound the alarm. This usually occurs 2 min before the loss of separation. ATCOs’ response to the STCA can be modelled using the
This calculation shows that the probability of not noticing the STCA alarm will be 0.12% (step 4 in Figure 2). Once the ATCO notices the STCA, they will make another effort to solve the conflict. This time, the appropriate GTT is
This calculation shows that, in complex traffic situation, the probability of a conflict not being solved under time pressure (STCA alarm) will be 69% (step 5 in Figure 2). In comparison, if the traffic is not complex, the probability of failure will be only 15%. Obviously, assessor should adjust the values of GTTs and EPCs to better suit the situation being assessed, so these probabilities are in no way final.
Finally, the probability of each outcome can be calculated by multiplying the probabilities of each event that led to that outcome. For example, if one wishes to calculate the probability that the conflict will be solved only after two failed attempts and an STCA alarm, step 5 in Figure 2, they should multiply probabilities of all events leading to that outcome as seen in Eqs. 10–12.
The last step in this process is to sum up all the probabilities of a favorable outcome (conflict solved) versus all the probabilities of an unfavorable outcome (loss of separation). In this example, the probability of the favorable outcome is 99.71% versus the probability of an unfavorable outcome which is 0.29%.
To better appreciate the effect of traffic complexity on the risk of human error, comparison with the traffic situation which is not complex can be made by excluding the traffic complexity EPC from the calculation. This calculation is omitted here for brevity, but the same method without the traffic complexity EPCs yields probability of a loss of separation below 3.5 × 10−5 per conflict (approximately 1 in 28,600 conflicts). That is two orders of magnitude less probable than in the case with moderate complexity (0.29% or 1 in 345). On the other hand, if the traffic is highly complex, the assessor might use higher EPC multiplier for complexity, all the way up to 1. In that case, the probability of an unfavorable outcome, i.e., loss of separation, is 2% (1 in 50) which is 7 times more probable than in the example above (Table 7).
Low complexity | Moderate complexity | High complexity | |
---|---|---|---|
0.999965 | 0.9971 | 0.98 | |
0.000035 | 0.0029 | 0.02 |
Comparison of probabilities to solve the conflict in traffic situations with different levels of complexity.
In addition to CARA, another method for assessing risks related to air traffic complexity is by conducting simulations. Simulation is a core method for ATM research and training, with different purposes requiring different levels of fidelity and simulation scope. Fidelity refers to the level of similarity between the simulated environment and the actual operations. Simulation scope can be broadly divided into strategic and tactical simulations. Strategic simulation tools (e.g., EUROCONTROL’s NEST) are used to analyze current and forecast future ATM situation on a global level. On the other hand, tactical simulation tools are used to accurately simulate ATC operations on a sector level (e.g., ATCoach by UFA or Micronav’s BEST Radar Simulator) [64]. For studies involving human factors, tactical real-time human-in-the-loop simulations provide the most reliable results.
Most representative results are produced when the simulator satisfies these requirements:
Realistic working environment
Accurate and versatile aircraft models
Representative ATC tool operation
Built-in stochasticity
Human voice communication
Research-level data logging
Suitable meteorological model
Suitable system and sub-system failure modelling
We used HITL simulations to assess the effect of trajectory-based operations (TBO) on air traffic complexity; for more information about that study, see [39]. Here we will provide a brief description of the methodology used and additional analysis of human errors made during that experiment. This will enable comparison of the simulation with the results obtained from CARA.
Simulation scenarios were developed based on the actual flight data. To measure complexity in conventional and trajectory-based operations, each simulation scenario had to be developed in three versions: conventional operations, 30% aircraft flying TBO, and 70% aircraft flying TBO.
Ten suitably experienced air traffic controllers were recruited to perform simulations. They all held professional air traffic controller licenses and had operational experience in Zagreb CTA Upper North sector (where the simulated traffic situations would take place). Before the actual experiment began, each controller received training in order to get accustomed with the simulator interface and operational procedures (though they were designed to closely resemble their actual working environment). The training consisted of an introductory lecture, pre-simulator briefing, simulator runs, and post-simulator briefing. One pseudo-pilot was used for all simulation runs. The controller could communicate with the pseudo-pilot only via voice communication (through headset) or data-link.
Each controller performed three scenarios for each of the three types of the operations, each corresponding to different traffic loads—low, medium, and high (9 runs in total).
During each simulation run, a subjective complexity measurement (SCM) tool opened every 2 min, accompanied by nonintrusive aural notification. The tool consisted of seven buttons (1–7), and the controller had to click on the one which was closest to the perceived level of air traffic complexity. The controller’s complexity assessment was time-stamped and stored.
In addition to the subjective complexity measurement scores, objective complexity indicators were also calculated in real time, time-stamped, and stored. For the purpose of calculating new complexity indicators post-simulation, all aircraft states were stored for each time step of the simulation (1 s). Aircraft state included all data that pertained to the specific flight at that point in time (e.g., position, velocity, heading, mass, pitch, bank, throttle, drag, climb mode, acceleration mode, assigned flight level/speed/heading, route, etc.).
All other available information was also stored. Human-machine interactions were recorded in-application, while an additional application was used to record radar screen and voice communication.
Overall, 88 simulator runs were performed, each lasting for approximately 50 min. Though it is very difficult to ascertain the number of potential and actual conflicts, the frequency of STCA alarms and loss of separation occurrences can be used to assess the risk that air traffic complexity introduces. Before going into further details, it must be noted that the probabilities presented herein are accurate only for this particular set of scenarios in this particular airspace controlled by these particular ATCOs, even if the sample size issues are disregarded. These probabilities should not be used for making real-life operational decisions and are presented here as an example of the human reliability analysis that can be produced from real-time HITL simulations.
In Figure 3, all 88 simulation runs are plotted, showing scenario complexity and number of STCA alarms for each. Blue dots represent simulation runs which had only STCAs, whereas red dots show those runs in which loss of separation also occurred. ATCOs were not allowed to give additional complexity scores once the loss of separation occurred, thus preventing that event from influencing their opinion. Separation minima were 5 NM horizontally and 1000 ft. vertically. Complexity scores were calculated as an average of the ATCO’s subjective complexity scores made during the peak 20 min of the simulation run [39]. Correlation coefficient between these two variables, complexity and number of STCAs, is 0.71, which indicates a somewhat strong correlation.
Number of STCA alarms vs. scenario complexity (red dots represent loss of separation).
First thing to notice is that most of the simulator runs, 58 out of 88, finished with zero STCAs. Of the remaining 30, only 5 were in
Next thing to notice is that, even though the complexity scores are highly subjective, it is very rare to have scenarios with complexity higher than 4 and no STCAs (only 4 out of 33 or 12%). This indicates that the ATCOs are bunching most of the scenarios into the lower half of the scale, perhaps underestimating the actual difficulty of managing the traffic situations.
In terms of HRA, it is interesting to calculate the probability that the STCAs will be resolved before the loss of separation occurs. Overall probability of human error in this case is only 0.155 (11 out of 71) compared to the figure calculated by CARA in the example presented in the previous section, which was 0.69. Surprisingly, this probability will not change much even if the scenarios were filtered by complexity. For example, for scenarios with complexity above 5, the probability of an STCA turning into a loss of separation is 0.175 (10 out of 57). For scenarios with complexity above 6, the probability is only slightly higher at 0.189 (7 out of 37). Here, ATCOs obviously show significant compensatory effects which should be included into CARA or modelled more precisely by assessors using the existing GTTs and EPCs.
On the other hand, the probability that the simulation run will contain at least one loss of separation rises sharply with complexity. For the lower half of the complexity scale, this probability is zero. If we consider all scenarios with complexity score equal to or above 4, the probability of loss of separation is 0.33 (11 out of 33). For scenarios with the score equal to or above 5, the probability is 0.5 (10 out of 20), and for scenarios with the complexity score above 6, the probability is 0.538 (7 of 13). This shows that even though the probability of an STCA turning into loss of separation is lower than expected by CARA, the number of conflicts rises to the level at which the loss of separation becomes extremely probable.
As for the
In this chapter we have shown how the air traffic complexity, through increasing the difficulty of finding the correct solution to the traffic conflict, influences human error probability and, consequently, risk in ATM as well. CARA HRA technique was used to show an example of calculation that can be used to assess the probability of a loss of separation in traffic situations with low, moderate, and high complexity.
Like other HRA techniques, CARA also relies on an expert assessor who must be able to correctly model the ATC operations by choosing the appropriate GTTs and EPCs. This process is very sensitive to small changes in the initial conditions because adding or omitting a single probability calculation often results in an order of magnitude different final probabilities. This problem is further exacerbated by uncertainty in modelling the ATC operations. For example, it is nearly impossible to determine beforehand how many opportunities to resolve a conflict will an ATCO have before a loss of separation occurs. In the example shown in Section 4.3, we used two attempts before an STCA sounded the alarm and one attempt afterwards. If any of those attempts were omitted, the probability of a loss of separation would have increased by a significant amount (up to 10 times). Furthermore, different ATCOs will use different strategies to solve a conflict, especially if the conflict solution implies secondary potential conflicts, which makes modelling of ATC operations in CARA even more difficult. This is not to say that CARA should not be used for HRA or as a part of broader risk assessment. It just means that CARA should be used with caution and that the results should be considered more as an indication of a risk instead of as an exact quantification of risk.
To better illustrate the accuracy of CARA and to show an additional method for risk assessment, we have presented a brief analysis of a simulation-based risk modelling. During the HITL simulations, which included complexity assessment, STCA alarms and loss of separation occurrences were identified and recorded. Expectedly, it was shown that the number of STCAs quite strongly correlates with the perceived level of air traffic complexity. More interesting was the fact that the probability of STCA turning into loss of separation was much smaller than the one predicted by CARA. Also, it almost did not change with the increase of complexity which indicates presence of strong compensatory effects.
On the other hand, the human error probability for a conflict, defined as a probability of a failure to solve the conflict resulting in a loss of separation, increases with the increase in complexity. Of all 88 simulation runs, zero losses of separation occurred in scenarios with complexity below 4 (55 simulation runs). However, for simulation scenarios with score above 6, loss of separation occurred in 54% of simulation runs. This increase can somewhat be explained by higher traffic loads, leading to more conflicts which then led to more occurrences of loss of separation. The truth is, however, that the increase in traffic was not such that the number of conflicts should rise to the levels achieved in the simulations. Simulation scenarios with high traffic load had only 15% more flights than scenarios with medium traffic load. It is the complexity of the traffic situation that precluded the ATCOs from being aware of all possible interactions and from solving the conflicts before it was too late. Though the sample size in the simulation study was quite small, it is clear that the model developed by the assessor in the CARA technique should be adjusted to reduce the probability of failing to solve the STCA.
In conclusion, both CARA and simulator study have a place in risk analysis in ATM. Best results are achieved when the simulations are performed to gather the probabilities of human error in a specific environment and when CARA is used to integrate the individual probabilities into a big picture assessment of ATM risks. The simulation study showed that the air traffic complexity is not only a large source of uncertainty but that it correlates nonlinearly with probability of loss of separation. This makes it difficult to model in common HRA techniques, with results having large error margins, but the greatest error would be to not model it at all.
Mammalian central nervous system (CNS) consists of brain and spinal cord. The neuronal network extends from the brain to all over the body and various neurotransmitters help transmit the message to target cells in different tissues. The part of the nervous system located in our gut is called the enteric nervous system (ENS). In all animals, gut brain axis is a lesser known nervous system so far. Our gut or “second brain” (ENS) chemically connects with the brain through neurons, secreted chemicals like hormones and neurotransmitters that send messages to the brain. The enteric nervous system’s network of neurons and neurotransmitters extends along the entire digestive tract – it starts from the esophagus to the stomach and intestines, and down to the anus. The “gut microbiome” comprises of microorganisms living in the gut (bacteria, viruses, and fungi) that can affect the chemical messages that pass between the gut and the brain. Microorganisms residing in the gut help regulate the body’s immune response. Since, the brain and the gastrointestinal (GI) system are intimately connected, therefore, they play key roles in certain diseases and to maintain our overall health to regulate cognitive and digestive behavior. The bidirectional communication between the brain and digestive system hence, are opening up avenues to think about diseases considering this angle.
Gut has recently become a subject of research in medical sciences wherein subjects with depressive symptoms, Parkinson’s and Alzheimer’s disease, autism, amyotrophic lateral sclerosis, multiple sclerosis, pain, anxiety and other neurodegenerative conditions are beginning to be looked to see what is going on in the gut. Effects of conditions like ulcers, constipation, and other GI problems have been a focus of research on aspects of brain functioning. The enteric microbiota impacts the gut brain axis (GBA), interacts locally with the intestinal cells and ENS as well as with the CNS through neuroendocrine and metabolic pathways. These studies suggest that GBA plays a vital role in maintaining mental health and can affect the feeding behavior when nutrient detection or absorption does not function properly as in case of metabolic conditions.
To ensure stability in the internal environment of body and drive adaptive changes, control mechanisms are key to animal’s survival. Recently GI has been recognized as a major source of signals modulating feeding behaviors, food intake, metabolism, insulin secretion and energy balance. Through its interaction with microbiota, it can shape our physiology and behavior in complex and sometimes unexpected ways. A growing scientific community has exploited the genetic amenability of
Easy genetic manipulation and effortless genetic tools make flies an insect of choice to study inter organ neuronal signaling including gut-brain axis neuronal connectivity. To understand human metabolic diseases and how a GI play a key role there is a recent focus of research. Some progressive studies also draw a link between neurodegenerative disorders and gut microbiota of humans and other insects.
Fruit flies have a simple and similar to humans- gut system (Figure 1). In mammals including humans, the esophagus (
Comparison between human and
The adult
Fly gut anatomy. (A) the
The fly midgut has been segmented into the anterior, middle and the posterior midgut (Figures 1,2A and B). It has been further subdivided morphologically and molecularly into 10–14 regions (Figure 2B) [11, 12, 13]. Midgut regionalization has been seen in the muscles, trachea and neurons that surround it [12, 13, 14, 15]. Physical properties (
The Malpighian tubules release at the junction between the midgut and hindgut (Figures 1,2A and B). The water/ion exchange occurs in the hindgut which consist of pylorus (a second valve-like structure), ileum, and rectum [7, 8]. The muscles that surround the epithelium in flies are striated, as opposed to the smooth muscles found in mammalian intestines [18]. An outer layer of longitudinal muscles found surrounding the midgut. Circular muscles are found to be present throughout the fly tract. Physiology of the intestine is maintained and regulated by autonomic innervation and by hormones. The tracheal system forms a branched structure surrounding the gut during development [15] and may influence epithelial regeneration in the adult. Owning to similarity of flies and human gut, we will be discussing further how gut of the flies is handled and controlled to understand about neural circuitry drawing it closer to the brain and diseases related to intestinal illnesses.
Both humans and fly intestines share similar tissue, anatomy and physiological function [19, 20]. Their gut are of endothelial origin in nature [21, 22] and comprise of an epithelial monolayer of columnar or cuboidal ECs. A series of sequential depressions called the crypts of Lieberkühn, along the small and large intestine, and protruding villi along the internal surface of the small intestine in mammalian intestinal epithelium maximize its surface area [23]. Extensive folding has not been reported in the
In both flies and mammals, the epithelial monolayer is associated on its basal side on an extracellular collagenous matrix (known as basement membrane) [29]. A checkerboard of innervated and trachea-oxygenated longitudinal and circular muscles tissue underneath the basement membrane in flies drive the peristaltic movements [30] (Figure 2C). Mammalian intestine has a similar organization of intestinal external musculature in the outer layers where musculature is also innervated and oxygenated by a plexus of vasculature [31, 32]. Layers including, the submucosa, (a dense layer of connective tissue containing nerves and lymphatic and blood vessels); muscularis mucosae (an additional muscle layer); and the lamina propria underling the intestinal epithelium and contains connective tissue, lymph nodes (Peyer’s patches), immune cells (leukocytes, and dendritic and mast cells), vessels and myofibroblasts [33], fill the space between the outer musculature and the basement membrane in mammals.
About 65% of human-disease causing genes are shared as a functional homolog in fruit flies. This shows conservation of genes and function at an evolutionary level. Fundamental processes such as digestion is also conserved from flies to humans.
The lineage of fly posterior midgut with only one type of mature absorptive cell and one main type of secretory cell is very simple. Although asymmetric ISC divisions in the fly midgut produce transient cells (EBs), these cells do not undergo further cell division and remain close to the ISCs before maturation. Fly midgut ISCs are situated basally and are broadly dispersed in the intestinal epithelium. The cellular composition and regeneration in
Like the regional specialization of digestive functions, the expression of digestive enzymes has also been found to confined to specific segments of the digestive tract in flies [12, 46, 47]. In addition to its roles in nutrient extraction and utilization, the digestive tract responds to the food and bacteria in its lumen. Digestion takes place in fly midgut [48] which can be further modulated by various factors like temperature, redox potential, pH, and intestinal transit [8, 48]. It has been shown that the expression and activity of digestive enzymes are tightly regulated in many insects like enzymes involved in the breakdown of sugars in flies are enriched in anterior (R1/R3) portions of the adult midgut and Peptidase genes express more posteriorly [47].
The enzymatic activity of the intestine is a key factor determining availability of certain nutrients. A substantial reduction of intestinal digestive enzyme activities (trypsin, chymotrypsin, aminopeptidase, and acetate esterase) has been reported in flies lacking EE cells [49]. Though not extensively investigated in
Changes in the expression of p38 kinase or the Atf3 and Foxo transcription factors cause accumulation of neutral lipid in ECs [58, 84]. It has been shown that neutral lipid increase following depletion of the EE hormone Tk [85], or in sterile female flies after mating [86]. It has been suggested that activation of intestinal lipogenesis is key to survival in diet-restricted flies. Indeed, nutrient scarcity induces expression of the sugar sensor transcription factor
Many animals generate localized regions of low pH inside the intestinal lumen to facilitate protein breakdown, absorption of minerals and metals, and limit the survival of ingested microbes. While mammalian digestion takes place in acidic conditions, insect digestion occurs at neutral or basic pH including
The contribution of five ion transporters enriched in the acidic region have been studied [88]. These include: the potassium/chloride symporter Kazachoc (Kcc), a member the Slc12 family of electroneutral cation-chloride transporters (express in intestine) [91, 92]; the Slowpoke pore-forming subunit of a calcium-activated K+ channel (express in neurons, muscles, tracheal cells, and two types of midgut ECs in the copper and iron cell regions) [93]; the ligand-gated chloride channel pHCL-2 which, in addition to regulating fluid secretion in malpighian tubules (express in the copper cell, iron, and large flat cell regions of the midgut) [94, 95]; the carbonic anhydrase CAH1; and the bicarbonate/chloride exchanger CG8177, belonging to the Slc4a1–3 subfamily of anion exchangers (express in a specific midgut pattern similar to that of pHCl-2) [96]. Collectively, these findings suggest that the transport of H+, Cl−, K+, and HCO3− contributes to acid generation in the
Flies extract water from their diet to maintain hydration and ionic balance. This compensates for substantial water loss resulting from metabolic and physiological processes. Although malpighian tubules are important for this process, but intestine also contributes. Water absorption from the food occurs in the insect midgut and in rectal pads of rectum [8]. The rectal pads are also the crucial site for reabsorption of ions. Ions and water can cross the intestinal epithelium through or between cells and their transport play an important role in the maintenance of ion gradients that sustain active transport in the intestinal epithelium. The scanning ion-selective electrode technique (SIET) provides a way to probe intestinal gradients for ions such as K+, Na+, H+, or Cl− [24, 97]. K+ and Na+ absorption occur largely in the large flat cell and posterior regions of the midgut and, also in the anterior hindgut in the case of Na+ [97]. The two
Metal ions such as copper, iron and zinc are essential micronutrients required for the correct folding and activity of a broad range of enzymes. The contribution of the intestine to metal homeostasis has not been extensively investigated but midgut regions, the Cu cell and Fe regions, are the most proposed sites of metal ion absorption. The Cu cell region turns bright luminescent orange upon Cu ingestion due to the fixation of copper by metallothionein [101, 102], and appears to be an important site of accumulation of ingested radioactively labeled Cu [101, 103]. The Fe cell region in R4a stains by Prussian blue and also accumulates exogenously administered radioactive Fe [101, 104]. Many studies have confirmed the roles for the Cu/Fe regions by exploring the molecular machinery involved in the intestinal uptake, intracellular trafficking, and efflux of metal ions.
Nutrient extraction and utilization may get affected by the passage of food along the alimentary canal and by its subsequent excretion. Transport of food to travel the entire length of the digestive tract takes less than 1 hour [105] in flies. As suggested, the amount of food retained in the crop is much larger in starved flies then refed flies than in flies fed
The ENS in humans, equivalent to GBA in flies is a part of the peripheral nervous system (PNS) that governs the running of the neurons which influence the GI. It is exploited nowadays in flies to understand more about how the two organs affect one another and lead to decisions regarding appetite, feeding mechanisms, taste preference and how to deal with hunger and satiety. How taste receptors detect different nutrients in the gut remains to be explored.
In humans, G-protein coupled receptors (GPCR) are involved in detection of five common tastes- sweet, salty, bitter, sour and umami. T1R family of taste receptors determine sweet and umami flavor. T2R family includes the bitter receptors [110]. Sweet taste receptors (T1R family) are found in intestinal tract as well as EE cells [111]. T1R1, T1R2, T1R3 and α-gustducin are expressed in the stomach, intestine and colon of humans and mice [112]. Cells of duodenal villi show co-localization of T1R1, T1R3 and α-gustuducin. Mammalian Gustducin protein is involved in bitter and sweet taste signaling and detection [113, 114]. α-subunit of gustducin is expressed in gastric cells and may play a role in nutrient detection [115, 116, 117] in rats and mice. Present in the mucosal lining of mammals and taste cells of the epithelium suggesting its possible role in taste uncovering on exposure to luminal contents [118]. Expression of T2R receptors in mouse GI tract including mT2R119 and mT2R108 has been looked into. mT2R119 expression is found in gastric and intestinal tissues, tongue and liver [115] like mT2R134 [118]. mT2R108, mT2R138 are present in the fundus, antrum, duodenum and tongue (not in liver) [119].
It has been found that sweet taste receptors stimulated in rat intestine influence and increase glucose absorption [120] through GLUT (glucose transporter) [121]. Presence of sugar in the diet galvanizes ECs into action to release hormones which in turn activate SGLT (Na+ / glucose cotransporter) [122, 123]. Similar results are shown in sheep where sugar receptor / sensor present on the luminal membrane stimulates SGLT1 via cAMP and G-protein dependent pathway [124]. Equine T1R2 (homologous to cows and pigs) is expressed on the luminal membrane of EE cells in the small intestine. In response to increased sugars, T1R2 along with T1R3, stimulates SGLT1 and enhances the ability of gut to absorb more glucose [125]. Analysis of in vitro line of ECs suggest that T1R2 and T1R3 detect sweet taste and exposure to sucralose increases release of hormones such as GIP (Gastric Inhibitory Polypeptide) and GLP (Glucagon like peptide) which further activate glucose activation and metabolism. An inhibitor of these receptors inhibits glucose metabolism suggesting these receptors present in the gut alter feeding mechanisms and post-ingestion decisions [120, 121].
Gustatory system in
Nutrient signaling and sensing are fundamental processes that animals including humans and flies undergo [131]. Proper coordination and communication between gut and brain is necessary to regulate metabolic homeostasis and physiology in all animals. In this regard, many research groups have shown the role of enteric neurons and endocrine signals as important mediators of these processes. The way the enteric nervous system communicates to the brain via neural circuits is a multifaceted question and poorly explored. In mammals such as humans, alterations in neuropeptides and brain – gut hormone levels can derail people otherwise on the path to a healthy life. These changes can also lead to diseases such as neurodegenerative diseases, metabolic syndrome and diabetes [132].
Gluconeogenesis is a biochemical pathway by which animals make sugars from non-carbohydrate precursors and sources [133]. It is used to regulate homeostasis and a stable internal state in post – fed state [134]. Studies in rats showed that stimulation of intestinal gluconeogenesis (IGN) sends a signal from sodium – glucose co- transporters present at the intestinal mucosa to the brain, initiating a neural gut – brain axis [135, 136, 137]. Diets rich in protein [138, 139, 140] and fiber [141] promote IGN stressing on the importance of nutrient sensing for initiating several gut – brain axis [137]. It has been found that μ – opoid receptors (MOR) regulate IGN. These receptors (present in the nerves in the portal vein wall) react to neuropeptides to stimulate a gut – brain neural circuit that affects IGN, hunger and satiety mechanisms [141]. Further analysis of MOR deficient mice shows the role of MORs in regulating food intake, referred as “reward” system [142, 143]. Analyses of MOR-knockouts (MOR-KO) demonstrate how they play a role in managing satiety effects of alimentary proteins, through a neural gut-brain circuit [140].
Vagus nerve (VN; pneumogastric nerve) is the longest cranial nerve [144] in humans which runs from the medulla oblongata in brain to colon in GI [145]. It innervates other structures as well such as larynx, pharynx, heart and lungs thus affects digestive, cardiovascular and respiratory system – all at one [146]. Vagal efferent send down signals from the brain to gut, which accounts for about 10% – 20% of all the nerve fibers. Remaining 80% is accounted for by the vagal afferents carrying information from the gut to the brain [147]. Vagal sensory neurons in the GI keep an eye on stomach volume and luminal contents through different neural circuits [148]. VN contains and branches into several sensory neurons (~2300 in mouse) that further innervate and render support and supply to other internal organs. A variety of sensory neurons, one side facing the brainstem and the terminal one facing the organ such as GI [149] have been revealed. Free terminals of vagal afferents are rooted within lamina propria of intestinal villi [148]. Some mammalian models like in cat and rat, it has been shown how these sensory neurons detect different nutrients in diets with the help of unambiguous and explicit fibers [150, 151, 152]. Vagal afferent endings in the intestine express several mechanosensitive as well as chemical receptors [153]. Glucagon- like peptide 1 (GLP1) is a gut hormone receptor that intercedes the nutrient sensing mechanism via VN [154]. GLP1R (GLP1 receptor) is present in many cells [155]. Agonists for GLP1R show how it affects brain further proving its presence in both, gut and brain [156]. Another receptor of vagal afferents, GPR65 near the intestinal villi, plays a role in nutrient detection drawing attention to how these sensory neurons are a part of the gut – brain axis [157, 158]. It detects serotonin and impact gut motility [147]. Such receptors detect several hormones present in the gut, like choleocystokinin (CKK), ghrelin and leptin which play a role in the regulation of hunger and satiety [159, 160, 161]. Because of its role in gut motility and mobility, VN and its afferent neurons present in the gut play a role in Intestinal Bowel Syndrome (IBS) [162] and new treatment plans around the same are being looked at in rat [162] and mice [158] models.
To take the findings in vagal nerves forward, nerves allowing communication of cNST (caudal nucleus of the solitary tract) with gut were focused on. Information about sugar detection to cNST via gut – brain axis is a topic of research nowadays. In live mice, it is noticed that glucose detection by cNST is robust and VN transaction silences that activation [163]. Nodose ganglion of vagus nerve when silenced prevents the sugar preference of cNST [163] suggesting the presence of a physical gut – brain axis. It has been shown that inactivation of sugar-activated cSNT prevents the mice to choose sugar from water or an artificial sweetener [163]. This study specifically calls attention to how organisms have paths for detecting nutrient signals, sensing them in the diet and also have circuitries to carry forward the signals and communicate with the rest of the body, purposely the brain.
With the help of several markers and reporter genes, it is found that
In the anterior portion of adult and larval midgut, serotonin positive neurites and various neuropeptides including Akh, Dh44, Myosuppressin, and possibly Allatostatin C and FMRFamide (or an FMRFamide-like peptide such as the NPY-like neuropeptide short neuropeptide F [sNPF]) [131, 168, 172, 180, 181, 182] have been described suggesting chemical diversity of enteric innervation. Four serotonergic neurons are found to innervate the enteric nervous system in fly larva. These neurons project from the antennal nerve (AN) near the SEZ and extend throughout the ENs [14]. These projections end at the anterior region of the midgut and are primarily around the proventriculus region and the foregut. These innervations are considered structurally analogous to the mammalian VN because of similar projections from the brain to the different structures of the foregut. It is yet to be seen if there are functional similarities as well [172].
Pigment- dispersing factor (Pdf), Ion transport peptide, and Proctolin positive neurites have been reported in the larval and adult hindgut [169, 183, 184, 185, 186]. All three innervated regions receive insulinergic innervation from the CNS; the
Innervation of adult
Functional studies of insect innervation have primarily focused on the control of peristalsis and peptide hormone secretion so far. Studies of peristaltic regulation in flies have primarily concerned the effects of neuropeptides (Allatostatins, Myosuppressin, or Drosulfakinins) on
Recently epithelial roles for gut-innervating neurons e.g. role in the control of fluid balance have been revealed by a semi-automated analysis of defecation behavior in adult flies, providing quantitative readouts for food intake, fluid/ion balance, and intestinal transit [14, 193]. The HGN1 neurons (2–5 CNS neurons located in the posterior segments of the abdominal ganglion) innervate the hindgut and the rectum (Figure 3), with a subset of their neurites projecting through the visceral muscles to reach the underlying epithelium [14]. HGN1 neuronal silencing experiments resulted in increased defecation rate. These neurons are shown to be required for the post-mating changes in intestinal fluid retention due to their epithelial innervation. It has been established that HGN1 neurons and the Pdf hindgut-innervating neurons have their direct action on the hindgut and anal sphincter muscles [192]. A role for gut-innervating neurons in the maintenance of epithelial turnover has also been suggested by the finding of anatomical proximity between enteric neurites in the posterior midgut and adult somatic intestinal progenitors, and the reduced ISC to EC differentiation resulting from downregulating Hedgehog (Hh) signaling (albeit pan-neuronally) [194]. The more anterior innervation of the proventriculus may also play a role in maintaining gut permeability. This is inferred from the finding that inactivation of a relatively broad subset of neurons, including a subset of anterior midgut-innervating neurons results in an abnormal proventricular structure, increased permeability of the epithelial barrier, and increased susceptibility to oral bacterial infection: all suggestive of defects in the production of peritrophic matrix [175].
In adult flies, inactivation of insulin-producing neurons results in contrasting effects on the hyperphagic response triggered by nutrient scarcity. Silencing of the insulin-producing cells of the brain PI that innervates the anterior midgut lowered this response, whereas silencing of the hindgut-innervating Ilp7 neurons increased it, and also resulted in higher circulating glucose [14, 195]. Not much is known about the importance of sparse sensory innervation of the intestine. One remarkable exception are the pharyngeal taste neurons.
Post-ingestive sensory feedback from the gut has been assumed to inhibit feeding based on work in other insects for example severing the recurrent nerve or the medial abdominal nerve, which transmit information from the gut to the brain, results in overconsumption in blowflies [199]. Work done in flies lends support to this idea; whereas severing the medial abdominal nerve did not disturb food consumption, severing the recurrent nerve elevated consumption of sucrose but not water or bitter tasting solutions [200]. The existence of neuronal stretch receptors on the gut that monitor the volume of ingested food is supported by both neurophysiological and anatomical data in numerous other insects [4, 80, 199, 201]. However, the existence and molecular nature of these receptors in
The gut–brain transmission systems involve both circulation-based endocrine-like and neuronal communication routes [205]. Neuropeptides (transmitters or neurosecretory) act as messenger molecules of enteric, sensory, autonomic and central neurons. Several peptide families have been found in both brain and gut. They act as neuropeptides and/or gut hormones and have significantly contributed to the understanding of gut brain interaction. Central and peripheral neurons together with EE cells in the GI tract and other endocrinologically active cells produce variety of peptides [206, 207, 208, 209, 210, 211] including hormones peptide YY (PYY) and pancreatic polypeptide (PP); neuropeptide Y (NPY), on the other hand [212]. These and other peptide families represented by glucagon-like peptide (GLP), ghrelin, cholecystokinin (CCK), corticotropin-releasing factor (CRF), leptin, osteocalcin and insulin (the last three are extra intestinal endocrine peptides) act on specific and genetically related groups of receptors that are expressed by distinct cells in the periphery and CNS. For their functional roles, endocrine peptides and neuropeptides are relevant for the regulation of digestion, control of food intake, metabolic homeostasis, and the impact of GI signals on sensation, emotion, affect, and cognition. Disturbances of the gut microbiota–brain axis result in changes of the expression and activity of many neuropeptides and their receptors in the CNS. Neuropeptides are therefore important secondary messengers of gut microbes in cerebral neuro circuitries that mediate the alterations in brain function and behavior that take place in response to changes in the GI microbial community [212]. Together it is emerging that neuropeptide systems such as NPY, CRF, ghrelin, and brain-derived neurotrophic factor (BDNF) play a particular role in the cerebral manifestations of gut microbiota perturbations.
In rats, choleocystokinin and glutamate neuro-epithelial circuit makes the communication between the brain and intestinal lumen possible stressing on the importance of a physical gut – brain axis [213]. It has been proposed that the physical connection between vagal nodose neurons and EE cells is present in rats which leads to regulation of gastrointestinal functions [213]. Intragastric nutrient infusion and optical readings of AgRP (Agouti-related protein) neurons in live and awake mice [214] suggest AgRP neurons affect long term homesostasis and energy balance of the body and do not get altered by minute changes in nutrient levels [215]. AgRP neurons get inhibited by high levels of satiation signals such as CCK and PYY (peptide YY). Receptors for both CCK and PYY are expressed by vagal afferent neurons’ terminals that innervate GI [159] suggesting a possibility of a physical connection between the gut and brain carrying the message from one point to another [216].
Apart from using neurons, fly intestine can also communicate with other organs through systemic signals. Intestinal physiology is modulated by both extrinsic hormonal signals (emanating from endocrine glands, neuroendocrine structures, or organs such as the fat body) and by its own peptide hormones, produced by EE cells. In turn, gut-derived signals such as EE cell-derived peptide hormones can have long-range effects on other internal organs. EE cells accounts for 5–10% of midgut epithelial cells in flies compared to 0.4–0.6% in the mammalian small intestine [217, 218, 219]. Majority of them express peptide hormones, often more than one and with regional stereotypy [219, 220, 221, 222]. The developmental program of EE cells shares similarities with that of neurons, probably reflecting a common phylogenetic origin [223, 224, 225]. Consistent with this idea, all known EE peptide hormones (exception insect CCHamides) [226] are also produced by the brain. Acting through these hormones, EE cells may play “neural-like” roles in regulating intestinal physiology and conveying intestinal as well as nutritional state to other cell types or organs. These roles are particularly prominent in the midgut given the relatively sparse innervation of midgut region.
A role for EE cells on muscle peristalsis has been suggested by the finding that ablation of Diuretic hormone 31 (Dh31)-expressing EE cells or Dh31 downregulation both reduce muscle peristalsis in the larval anterior midgut, which may function as a valve to minimize mixing of acidified and non-acidified food in the acidic region of the midgut [227]. Adult EE cells produce Bursicon. Signaling through the Bursicon/DLGR2 receptor in visceral muscle, represses the production of the visceral muscle-derived mitogen Vein and, consequently, ISC proliferation. Another study found in
A high-sugar diet leads to increased midgut EE cell number and enhanced production of EE-derived Activin ligand (Activin-β not Daw) [230] suggesting systemic roles for EE-derived peptide hormones. Mirroring the activin-mediated fat-to-gut signaling involved in sucrose repression, midgut-derived Activin-β binds to the TGF-β receptor Baboon in fat cells which, in turn, leads to enhancement of Akh signaling it the fat body and consequent hyperglycemia [230]. CCHamides are insect hormones [231, 232] and their expression is promoted by nutrient availability and sites of expression include the gut EE cells, a subset of central neurons and, possibly, the fat body [226, 233, 234]. Their receptors are expressed in the nervous system including the insulin-producing neurons, and are absent from the gut [226, 234]. Although not strictly gut-derived, a new peptide hormone produced not by EE cells, but by an adjacent secretory gland may have provided the most compelling example to date of gut-to-brain communication. Indeed, Limostatin (Lst) peptide is produced by the
In animals with a vascular system, peptides secreted from EE cells can enter the bloodstream and reach tissues at a considerable distance, ranging from other cells in the digestive tract to brain centers regulating appetite [236]. Nutrient availability can also affect the number of EE cells; signaling through the nuclear hormone receptor Hr96, dietary lipids control EE differentiation during the first few days of adult life, providing another way to couple nutrient availability with tissue architecture and physiology [237]. Modulation of intestinal physiology by systemic signals has also been looked into [220, 221]. Control of epithelial turnover by insulin-like peptides or JH (juvenile hormone), and the coupling of dietary availability of sugars with EC digestive enzyme production via the fat body-derived Activin ligand Daw are some examples. The actions of the diuretic peptide Leucokinin (Lk), secreted into the circulation from CNS-derived nerves that terminate at the abdominal wall [14, 238, 239] is an another example. Downregulation of either this peptide or its receptor leads to abnormal excreta and extreme fluid retention that can rupture the abdominal wall [14]. Finally, a link between energy balance, intestinal permeability, and immunity has been suggested by the finding that sNPF is a target of the Crtc/CREB energy sensing pathway, and functions to maintain epithelial barrier integrity acting through its receptor in ECs [240]. Although the precise source of sNPF remains to be established, tissue-specific genetic and expression data points to roles in neurosecretory cells [240], consistent with roles as a neuroendocrine hormone or in gut-innervating neurons. Gut can also produce long-range signals to affect the physiology of other organs, for example by production of the signaling protein Hh by larval EC. Circulating Hh regulates developmental timing by controlling ecdysteroid production in the prothoracic gland, and is required for mobilization of fat body TAG stores during starvation [241].
Other functions of brain-gut peptides and hormones include detection and utilization of nutrients during hunger, stress or normal conditions. Diuretic hormone 44 (Dh44), a homolog of the mammalian corticotropin –releasing -hormone (CRH) activate by nutritive sugars. Disturbed activity of Dh44 neurons leads to fail to select nutritive sugars [131]. These neurons localized to PI in adult brain, counterpart of mammalian hypothalamus [242] are filled with neurosecretory cells [131]. Dh44 conveys information from Dh44 neurons to Dh44 receptor R1 neurons in the brain and R2 cells in the gut suggesting requirement for nutrient selection. Artificial activation of these neurons causes rapid PER and it has been suggested that Dh44 is necessary and sufficient for gut motility and excretion in flies [131]. Both Dh44 neurons and the gut-innervating insulin-producing neurons of the PI are innervated by Hugin-producing neurons that suppress food intake and induce locomotion, providing a possible link between food-related behaviors and intestinal physiology [243]. It is seen later that Dh44+ neurons rapidly activate during amino acid feeding and are a direct sensor of dietary amino acids [244].
Fly gut peptide Dromyosuppressin [181] expresses in the number of cells in central nervous system (CNS) in adult stage, extending into the rectum, near the anus; part of the adult gut. Their immunoreactive fibers also project into the crop and show expression of Dromyosuppressin [172] and crop abundantly expresses Dromyosuppressin receptors (Dromyosuppressin receptor I) [220]. The effects of neuropeptide on neural regulation of crop motility and contractions have been shown [245]. Serotonergic neurons have also been shown to regulate insulin producing cells (IPC) located in the PI of the adult brain of the fly [246]. DILP2, 3 and 5 express particularly in midgut [187, 220, 247] and extend their axons to proventriculus, crop and corpora cardiaca [248]. DILP2 is particularly involved in carbohydrate metabolism [249]. Decreased levels of DILP2 affects stored trehalose as well [248]. IPC knockdown flies show increased glycogen storage, high levels of circulating triglycerides and extended lifespan [248].
Mammalian neuropeptide NPY (Neuropeptide – Y- precursor) has an invertebrate homologous peptide called NPF due to characteristic C- terminal F residue [250]. NPF has been shown to co- localize within midgut cells in
Other neuropeptides include Allatostatin characterized into three kinds namely Allatostatin A/B/C [258, 259, 260]. The endocrine cells producing Allatostatin are found in the posterior midgut and is innervated by axons from thoracico-abdominal ganglion [220]. Its receptors, DAR1 and DAR2 are predominantly located in the central nervous system and gastrointestinal tract (including crop, midgut and hindgut) respectively [261, 262]. Allatostain is also present throughout midgut. Another major peptide is PDF. It is expressed in central nervous system [263] and its neurons are also found in thoracico-abdominal ganglion [184]. Axons from these neurons innervate midgut and hindgut and in the crop. These neuropeptides are closely associated to circadian rhythm [264] and locomotor activity as well.
Various studies have suggested that intestinal health has a significant impact on neurodegeneration. Specifically, dysregulation of GBA cross talk has been associated with metabolic syndrome [265, 266] and psychiatric disorders. GBA is largely mediated by CNS, ENS and intestinal microbiota. With its much simpler gut microbiota (1–30 taxa) as opposed to vertebrates intestine (1–500 taxa) [267],
Most genes and pathways that play crucial roles in metabolic diseases are conserved between flies and humans [268]. Diet-induced obesity in flies is associated with many of the pathophysiological consequences found in humans, including hyperglycemia, insulin resistance, cardiac arrhythmia and fibrosis, reduced longevity [269, 270] and nephrosis [271]. The gut is crucial for peripheral body fat storage and serves as a major site of dietary lipid absorption and absorbs other dietary macronutrients (sugars, proteins and fats). It also metabolizes both glucose and lipids into metabolic intermediates, which after loading into hemolymph get used in other tissues and organs. In flies, lipoprotein complexes containing apolipophorins carry sterols and diacylglycerols from the gut to other tissues [81]. Fly lipoproteins also contain Hh, a cholesterol-linked, gut-derived ligand that binds the transmembrane receptor Patched on fat body target cells to promote lipolysis during larval starvation [241, 272]. The human anti-obesity drug orlistat, a gastric lipase inhibitor, has been shown to reduce body fat accumulation in adult flies [56]. Supporting a crucial role for lipolysis, midgut lipid accumulation and global fat storage are reduced by the insulin signaling pathway inhibitor Foxo in enterocytes, via reducing the expression of
The gut microbiota and its metabolism plays an important role in modulation of fat storage in the fly. As seen in humans, fly gut is enriched in
Recently
In the recent past, data from the intestinal mechanisms found in flies have been shown to be active in mammals, and may therefore become relevant in the context of human pathologies including diabetes, obesity, neurodegenerative diseases, gastrointestinal cancers, or aging. Parallel findings of communication between gut and brain via neuropeptides in
Developing new techniques and behavioral assays can help us explore physiological drives: what is the gut function to maintain the overall health of the animal. It would be interesting to find out the key intestinal sensors. How the physical association between gut and brain via neural micro circuits regulate decisions regarding nutrition, hunger and satiety is under question. Better “holistic” and quantitative methods, integration of spatial and temporal information about genetic events more comprehensively are required, so that cause and effect can be uncoupled in a physiological context [306]. We anticipate and hope that fly models of intestinal pathology, in addition to uncovering newly identified genes (chemosensory and others) and basic biology mechanisms will emphasize the most conserved aspects of human intestinal biology. As a result, fly will contribute to translational research investigating drug effects, and microbial and host genetic component analyses, leading to biological findings that are broadly applicable to human health and disease.
The authors declare no conflict of interest.
This work is supported by Wellcome Trust/DBT India Alliance Fellowship (grant number IA/I/15/2/502074) awarded to PK.
ZS, SK and PK all substantially contributed to the conception and design of the work. Everybody participated in drafting and revising the work, made the figures, wrote the chapter and approved the final version for publication.
ENS | Enteric nervous system |
GI | Gastrointestinal |
GBA | Gut brain axis |
CNS | Central nervous system |
ISCs | Intestinal stem cells |
ECs | Enterocytes |
EE | Enteroendocrine |
EBs | Enteroblasts |
TA | Transient amplifying |
AMP-5′ | Adenosine monophosphate |
AMPK- 5’ | AMP activated kinase |
TAG | Triacylglyceride |
Sug | Sugarbabe |
CD36 | Cluster of differentiation 36 |
Npc1 | Niemann-Pick C1 |
SREBPs | Sterol regulatory element-binding proteins |
Akh | Adipokinetic hormone |
RNAi | RNA interference |
Kcc | Kazachoc |
SIET | Scanning ion-selective electrode technique |
Dh44 | Diuretic hormone 44 |
GPCR | G-protein coupled receptors |
PNS | Peripheral nervous system |
GLUT | Glucose transporter |
SGLT | Na+/glucose cotransporter |
GIP | Gastric inhibitory polypeptide |
GLP | Glucagon like peptide |
TK | Tachykinin |
MOR | μ – opoid receptors |
IGN | Intestinal gluconeogenesis |
MOR-KO | MOR-knockouts |
cNST | Caudal nucleus of the solitary tract |
GLP1 | Glucagon- like peptide 1 |
sNPF | Short neuropeptide F |
AN | Antennal nerve |
Ilp7 | Insulin-like peptide 7 |
VN | Vagus nerve |
IBS | Intestinal bowel syndrome |
HGN1 | Hindgut neuron1 |
Hh | Hedgehog |
Poxn | Pox-neuro |
Dh31 | Diuretic hormone 31 |
Lst | Limostatin |
Lk | Leucokinin |
JH | Juvenile hormone |
Crtc/CREB | cAMP-regulated transcriptional co-activator/ cyclic AMP-responsive element-binding protein |
SEZ | Suboesophageal zone |
CRH | Corticotropin –releasing -hormone |
PI | Pars intercerebralis |
IPC | Insulin producing cells |
DILP | Drosophila insulin- like- peptide |
5-HT1A-5 | Hydroxy tryptamine /serotonin |
AKH | Adipokinetic hormone |
PYY | Peptide YY |
AgRP | Agouti-related protein |
CCK | Choleocystokinin |
PP | Pancreatic polypeptide |
NPY | Neuropeptide Y |
Pigment- dispersing factor | |
CRF | Corticotropin-releasing factor |
BDNF | Brain-derived neurotrophic factor |
SCFAs | Short-chain fatty acid |
AMP | Anti-microbial peptide |
ROS | Reactive oxygen species |
DUOX | Dual oxidase |
IMD | Immune deficiency |
APC | Adenomatous polyposis coli gene |
CRC | Colorectal cancer |
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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 26th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:289,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/70104",hash:"",query:{},params:{id:"70104"},fullPath:"/chapters/70104",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)}()