A-PiMod high level project goals.
Chapter 1: "Permanent Maxillary and Mandibular Incisors"\n
Chapter 2: "The Permanent Maxillary and Mandibular Premolar Teeth"\n
Chapter 3: "Dental Anatomical Features and Caries: A Relationship to be Investigated"\n
Chapter 4: "Anatomy Applied to Block Anaesthesia"\n
Chapter 5: "Treatment Considerations for Missing Teeth"\n
Chapter 6: "Anatomical and Functional Restoration of the Compromised Occlusion: From Theory to Materials"\n
Chapter 7: "Evaluation of the Anatomy of the Lower First Premolar"\n
Chapter 8: "A Comparative Study of the Validity and Reproducibility of Mesiodistal Tooth Size and Dental Arch with the iTero Intraoral Scanner and the Traditional Method"\n
Chapter 9: "Identification of Lower Central Incisors"\n
The book is aimed toward dentists and can also be well used in education and research.',isbn:"978-1-78923-511-1",printIsbn:"978-1-78923-510-4",pdfIsbn:"978-1-83881-247-8",doi:"10.5772/65542",price:119,priceEur:129,priceUsd:155,slug:"dental-anatomy",numberOfPages:204,isOpenForSubmission:!1,isInWos:null,hash:"445cd419d97f339f2b6514c742e6b050",bookSignature:"Bağdagül Helvacioğlu Kivanç",publishedDate:"August 1st 2018",coverURL:"https://cdn.intechopen.com/books/images_new/5814.jpg",numberOfDownloads:7278,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfDimensionsCitations:3,hasAltmetrics:0,numberOfTotalCitations:4,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 4th 2016",dateEndSecondStepPublish:"October 25th 2016",dateEndThirdStepPublish:"July 16th 2017",dateEndFourthStepPublish:"August 16th 2017",dateEndFifthStepPublish:"October 16th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,editors:[{id:"178570",title:"Dr.",name:"Bağdagül",middleName:null,surname:"Helvacıoğlu Kıvanç",slug:"bagdagul-helvacioglu-kivanc",fullName:"Bağdagül Helvacıoğlu Kıvanç",profilePictureURL:"https://mts.intechopen.com/storage/users/178570/images/7646_n.jpg",biography:"Bağdagül Helvacıoğlu Kıvanç is a dentist, a teacher, a researcher and a scientist in the field of Endodontics. 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With increasing duty time and traffic growth, pilots can benefit from an ‘experience aid’. Ideally, the crew and the ‘experience aid’ (or assistance system) comprise a cooperative system [1, 2]. This cooperative system approach follows the cognitive systems engineering frameworks, as advanced by Hollnagel and Woods [3].
In its current form, automation does not take into consideration the pilot’s cognitive and emotional state. Rather, automation provides assistance based on explicit and static task assignments, with no adaptive capabilities, even though it is capable of providing higher or lower levels of support depending on the crew state and/or complexity of the operational situation [1, 2, 4]. It is argued that safety is optimised when human and automated systems adapt both to each other and to the specific operational context. This guarantees fluent and cooperative task achievement—maintaining safety at all times.
This research was undertaken as part of the Applying Pilots’ Model for Safer Aircraft (A-PiMod) project, funded by the European Commission (Framework Programmes for Research and Technological Development) [1]. The aim of this project was to demonstrate a new approach/concept (and associated technologies) for an adaptive automation and multimodal cockpit which might mitigate and/or reduce human error. The project commenced in September 2013 and finished in September 2016.
Currently, Pilots share responsibility for different flight tasks with cockpit systems. As defined by Kaber and Prinzel, adaptable systems are systems which require human delegation of task and ‘function authority’ to automation during real-time operational performance (i.e. the task distribution is controlled by the user) [5]. This is different to adaptive automation (AA), which allows for dynamic changes in control function allocations between a machine and human operator based on states of the collective human–machine system’ [6, 7].
Human factors problems with automation have been cited as contributory factors in many air accidents. This includes: Flight Air France 447 (2009) [8], Flight Spanair 5022 (2008) [9], Flight Helios Airways HCY 522 (2005) [10], Flight China Airlines 140 (1994) [11], and Flight Air Inter 148 (1992) [12]. The air accident reports highlight several human factors issues such as automation surprises, reduced situation awareness, workload problems and over-reliance on automation [1].
Errors are defined as ‘actions or inactions by the flight crew that lead to deviations from organisational or flight crew intentions or expectations’ [13]. Unmanaged and/or mismanaged errors frequently lead to undesired aircraft states. Flight crew errors reduce the margins of safety and increase the probability of adverse events [13]. As documented by the International Civil Aviation Organisation [14], human error is a causal factor in between 60%-80% of accidents and serious incidents. It is stated in the ‘Flightpath 2050, Europe’s Vision for aviation’, that ‘the occurrence and impact of human error’ will be ‘significantly reduced through new designs and training processes and through technologies that support decision making’ [15].
In line with a cooperatives systems approach, the question of automation status/communication and its role in the execution of the flight, should to be framed from a team perspective and linked to a risk assessment of the mission and the selection of a course of action. Thus, the theoretical starting point for addressing human factors issues with automation (specifically, teamwork, task distribution, authority, situation awareness, error detection and workload management), is the assessment of the crew/automation/aircraft/environment state in relation to the achievement of the mission level goal (i.e. mission level risk assessment), and the identification of a suitable task distribution at cockpit/agent level, to achieve this [1, 2]. So conceived, automation has a role in relation to (1) real-time risk assessment, (2) the identification of a course of action, (3) the selection and subsequent implementation of a course of action, and (4) the identification of an appropriate task distribution based on the crew state [1, 2].
Further, it is argued that there is relationship between addressing human factors issues with automation (specifically, teamwork, situation awareness, task distribution, authority, error detection and workload management) and improving crew interaction with cockpit systems. The provision of a multimodal concept can support the above. In addition to (1) allowing for flexible interaction with cockpit systems and (2) providing a means to communicate with the crew in relation to crew state and decision support, the multimodal cockpit has a role in relation to (3) supporting the better assessment of crew state/workload (information inputs re crew activity/interactions).
Stakeholder involvement in programme evaluation has been recognised as one of the most effective approaches to enhancing the use of evaluation findings, and ensuring the validity of the evaluation activities [16]. Stakeholder involvement is defined as the participation of (programme) stakeholders in any phase of an evaluation [17]. Stakeholder involvement can vary with regard to diversity in stakeholder selection for participation, the control of technical evaluation decisions and the depth of stakeholder participation in the programme/project evaluation process [18]. Stakeholders are conceived as invaluable source of knowledge, perspectives, information on context and needs. Drawbacks of stakeholder involvement are also reported. This includes the feasibility of implementing a successful participative study. For example, time, cost, involvement from (disadvantaged) groups and skills required from an evaluator in facilitation and ‘good listening’ [19].
The involvement of stakeholders to accomplish given tasks by participating in common activities has been central to ‘Community of Practice’ concepts [20]. ‘Community of Practice’ members engage in a set of relationships over time around some particular area of technical knowledge or skill associated with the given tasks. This allows the members of a specific ‘Community of Practice’ to generate a sense of joint enterprise and identity by sharing a practice—doing things together, developing a sense of place, common goals. In Wenger’s analysis, three characteristics are crucial to define a ‘Community of Practice’: (1) the ‘domain’—which specifies the identity of COPs with the specific competence and commitment the stakeholders engage; (2) the ‘community’—stakeholders build their relationship interacting in joint activities, sharing information and common objectives and learning from each other; and (3) the ‘practice’—stakeholders share a repertoire of resources (experiences, stories, tools, ways of addressing recurring problems) which help forming the practice with time and sustained interaction [20].
The aim of the A-PiMod project was to demonstrate a new adaptive automation concept (and associated technologies) enabled by a hybrid of three elements: (1) multimodal pilot interaction, (2) operator modelling and (3) real-time risk assessment. It is anticipated that the introduction of this new automation concept will reduce human error—making substantial progress in relation to aim of reducing the accident rate by 80%. Table 1 below provides a description of the high level project goals
No | A-PiMod high level project goals |
---|---|
1 | To reduce accident rate by 80% |
2 | To achieve a substantial improvement in the elimination of and recovery from human error |
3 | To mitigate the consequences of survivable accidents |
4 | To support smart pilot assistance |
A-PiMod high level project goals.
The overall design/evaluation methodology combines formal HMI design/evaluation activities (i.e. interviews and simulator evaluation), informal HMI design/evaluation approaches (i.e. participatory design activities), along with an integrated stakeholder approach to evaluation [4]. Overall, 23 COP sessions and two phases of simulator evaluation have been undertaken. The first phase of simulator evaluation involved eight participants, while the second phase involved 12 participants. This has been reported in more detail in [1, 2].
The concept of ‘Community of Practice’ as proposed by Wenger [20] underpins the A-PiMod ‘Community of Practice’ approach. The A-PiMod ‘Community of Practice’ involved stakeholders who shared technical knowledge and skills associated with relevant functions in the Air Traffic Management (ATM system), and broader aviation related domain. Overall the role of participants in the A-PiMod ‘Community of Practice’ concept was characterised as a ‘participatory’ approach. Members engaged in a range of validation/evaluation activities on a continuous/regular basis, through the run-time of the project.
The panel of stakeholders in A-PiMod included both ‘primary users’ (i.e. internal stakeholders representative of each project partner), and ‘all legitimate groups’ (i.e. external stakeholder representative of the aviation-related industry and Flight operational system). Stakeholder participation involved consultative interaction along with engagement in technical research tasks.
Figure 1 below depicts the composition of the community of practice – with attention to the levels of expertise of both internal and external stakeholders. Internal stakeholders are represented in blue. External stakeholders are represented in amaranth. The overlapping levels of expertise are indicated by the red dotted line.
A-PiMod Community of Practice: Stakeholder Competency and Knowledge (Source: Cahill et al. [2]).
Two sets of simulator evaluation were undertaken. In both cases, the evaluation involved a mixed-method approach with the administration of semi structured interviews, simulator observations, collaborative workshop sessions and questionnaires. Each set of evaluations involved two crew members and elapsed over 2 days. Overall, a scenario-based evaluation approach was adopted. Simulator evaluation involved the use of the DLR simulator—the GECO system.
In day 1, participants were first briefed about the overall procedures, consent was obtained, and profile information was elicited. Then participants undertook a semi-structured interview regarding the current state of automation and HF still-open issues. After this both participants obtained training, in relation to interacting with the new adaptive automation multimodal concept/technologies in the simulator. The training was delivered with the support of slides, software training tools, and some hands-on training in the simulator (i.e. using the A-PiMod user interface displays such as the MC-M Display and the MultiModal ND). Then, hands-on training in relation to using the GECO simulator was provided. After this training session, participants undertook the specific simulator evaluation session, addressing the adaptive automation and multimodal concept/technologies. After the simulator sessions, participants were asked to complete a questionnaire evaluating the adaptive automation and multimodal concept and technologies. Further, a specific simulator session was undertaken with a focus on the interaction with the MultiModal ND. This involved a collaborative session to evaluate the operational and safety aspects pertaining to the multimodal cockpit.
In day 2, participants were asked to evaluate the adaptive automation and multimodal concept and technologies. This involved individual post evaluation individual semi-structured interviews. Then a collaborative workshop session was undertaken. This focussed on three distinctive scenarios (i.e. (1) supporting routine operations, (2) avoidance of conflict of taxi/way or apron, (3) Incapacitation (VETO)) and to what extent A-PiMod could support the Pilots in relation to workload management, error identification, situation awareness, teamwork, and how this would have an impact on the error reduction. After this, the participants completed an individual questionnaire related to benefits analysis.
In order to assess the potential safety impact of the new automation concept (and allied multimodal cockpit), a systematic approach was applied. This involved the elicitation of structured feedback from pilots and experts, using the Total Aviation Risk model [21]. Specifically, structured feedback was captured in relation to potential change factors for base events in this risk model [21]. The total aviation system risk model contains 425 base events and 51 end states. The particular structure of an event sequence diagram and its connected fault trees depends on the scenario considered. The probability change factors of all impressionable base events are determined in the safety assessment on the basis of information gathered in multiple workshops with members of the Community of Practice (COP). In the workshops the participants discussed the kinds of mechanisms facilitated by the innovative concept which may increase or decrease the probability of a particular base event in a scenario. The specific details of the quantification of safety impact have been reported in another paper [22].
Overall, the objective is to provide assistance to the flight crew when and if required. Automation acts as a third crew member providing information and task support to crew –safeguarding the mission level goal [1, 2].
The third pilot approach involves providing (1) decision/information support, and (2) workload support. This follows from the hypothesis that information underpins good decisions, which in turn results in safe aircraft states. Further, it follows the philosophy that if there in increase in workload, certain functions should be shifted to automation, to reduce the cognitive/physical burden on the crew.
The overall approach involves continuously monitoring the operational situation and the allied crew/automation state, to determine the best distribution of task activity between the crew and automation [1, 4]. It is anticipated that this will ensure the safe completion of the flight. If there is an increase in workload, certain functions will be shifted to automation, to reduce the cognitive/physical burden on the crew. Also, automation is used to support information management tasks (i.e. information gathering and information assessment). This in turn has an impact on safety. Real-time feedback is provided to the Flight Crew via a cockpit user interface (i.e. HMMI Interaction Manager), so that the crew at all times understand the status of (1) the operational situation, and (2) the joint crew automation system. This ensures full situation awareness, which in turn impacts on mission safety. All of the above ensures that the aircraft remains in a safe state. This in turn has consequences at a process level (i.e. both single and multiple flight levels).
The team comprises the Flight Crew (namely the Pilot Flying and the Pilot Monitoring), the ‘Third Pilot’ and automation. Accordingly, the third pilot is conceived as a virtual team-member [1, 2, 4]. All team members co-operate in relation to making and executing mission level decisions. Mission level decisions are enacted at the mission level and translated into new cockpit level tasks that have to be distributed between the crew and automation, and then performed by them. The system continuously monitors the operational situation and the allied crew/automation/aircraft state, to determine the tasks the team has to perform together, and how to best distribute them between the crew and automation.
The new cockpit technology (i.e. automation and associated systems) allows us to answer the following questions:
Is the joint crew/automation system in a safe state?
Is there a potential for a safety critical aircraft state (i.e. now and/or the near future)?
Do the crew (Pilot Flying and Pilot Monitoring) require support in terms of increased levels of automation?
Do we need to adjust the level of automation?
Do the crew require information/decision support?
A-PiMod flags potential risks—providing operational guidance in relation to managing those risks. Pilots have final control, but are responsible and accountable for their decisions and actions [1]. The crew are responsible for assessing the risk status of situation and the appropriate course of action. As such, the crew are not required to follow the decision support provided by the third pilot. This decision support is an aid, not a requirement. The crew can over-ride system proposals/decisions, except in certain critical situations (i.e. incapacitation).
A-PiMod adopts a team centred approach as opposed to a crew centred approach. Specifically, is assists the Flight Crew in relation to information and workload management (i.e. intervention if over and/or under loaded). Automation is conceived as an extension of the pilot’s ability to carry out an action(s). Automation provides decision and information support. In principle, we are focussing on the outcome. That is, we are considering what is best for the safe and efficient completion of the mission, as opposed to adapting to human needs. As indicated in the architecture concept (see below), if the Pilot Flying/Pilot Monitoring is overloaded and this threatens the completion of the mission, the task distribution is adapted at the agent level [1, 2].
As reported previously, we propose partnership as opposed to dynamic changes in control function (such that changes can be managed autonomously by the system) [1]. The A-PiMod architecture describes the means for the adaptive completion of Mission Level tasks and their distribution between the crew and automation. This includes the real-time analyses of both the pilot’s state (situation awareness and workload) and mission risks [1]. Apimod will permanently assess what has to be done by the cockpit (mission and its context = > cockpit level tasks), distributing it between the crew and automation, and assessing if these agents are correctly performing the tasks they have been assigned (i.e. recover from a stall, avoid ground obstacles, etc.). Task distribution is the product of a situation management process [1]. Here, the pilot flying and pilot monitoring, along with other automated processes cooperate to assess the situation, its risks, what has to be done (cockpit level tasks) and the associated risks [1]. This results in a task distribution.
As described in the architecture concept, in situations where the flight crew are overburdened, task distribution is adapted at the agent level [1]. Automation adapts to crew states and capabilities, so that all required cockpit-level tasks are performed [1]. It is this architecture that guarantees the safe completion of the flight.
The proposed automation concept addresses the key decision requirements as defined in the safety case [1, 2, 4]. For more information, please see Table 2 below.
No | Decision requirement | How supported by automation |
---|---|---|
1 | Authority | Boundaries for automation set in relation to role of Pilot and associated decision authority |
2 | Information | Supports information acquisition and analysis |
3 | Time | Automation provide real time updates as to status of situation (both current and future) so have time to anticipate potential future problems and how might manage them |
4 | Judgement | Automation provides decision assistance – providing feedback on potential course of actions and risk associated with each. Automation can gather decision support information from actors outside the cockpit – if collaborative/consultative decision |
5 | Teamwork | Human agents/crew and automation are conceived as a team Better collaboration between team members – enhance situation awareness and increase safety Cockpit as a cooperative system Support teamwork in terms of information sharing: (1) information sharing between crew and automation, and (2) information sharing between cockpit and agents outside the cockpit |
6 | Feedback | Automation provides feedback (updated information picture) in relation to the outcome of decisions taken and next steps |
Automation provides support for decisions.
Underpinning the Third Pilot concept is the A-PiMod multimodal cockpit concept [1, 2]. The multimodal cockpit (1) allows for monitoring of crew interactions with cockpit systems, (2) supports crew communication with automation (i.e. via the MCM Display) and (3), enables voice and touch interaction with cockpit displays [1, 2].
The A-PiMod architecture allows for (1) adapting the mission based on current circumstances and (2) the subsequent organisation of the cockpit (task distribution between the crew and automation) and (3) the circulation of information between the crew and cockpit systems (including automation) to the current and future situation(s) [23]. The starting point in the architecture is to adapt the mission permanently, and to translate a given mission configuration (e.g. decision to divert to closest airport) into tasks that have to be executed by the ‘team’. The tasks are then distributed between the team members (crew and automation) based on their current states and capabilities.
Figure 2 below depicts the A-PiMod architecture. This is based on a three layer hierarchy of tasks [23].
Task Hierarchy.
The architecture is a ‘control’ architecture; it’s about the distributed control of a given object. In this example, this refers to the overall aircraft state including navigation and trajectory. Tasks at a given level are translated into the tasks of the level below based on the context of their execution [23]. At the mission level, the context of execution includes (1) the state of the aircraft and (2) the traffic and environmental context in which the aircraft is flying (i.e. weather, ATC, traffic). At the cockpit level, context refers to (1) the status of the cockpit agents (namely pilots and automation) and (2) cockpit equipment (i.e. displays).
As detailed in Table 3 below, this control architecture is elaborated in relation to three levels. This includes the mission level, the cockpit level and the agent level. These levels are hierarchical—each level is a decomposition of the level above. All actions occur at the agent level. The upper levels are about deciding what tasks the different agents perform, based on the current contexts, at the mission and cockpit levels.
Level | Description |
---|---|
Definition of mission level tasks | Single flight level—mission phase/sub-phase, context, A/C state—the context the aircraft is in |
Definition of cockpit level tasks | Tasks that need to be performed by all (i.e. crew and automation) to achieve mission goals given specific context elaborated |
Definition of agent level tasks | Actions undertaken by crew or automation to achieve mission goals in specific context |
Architecture levels.
The key level is the cockpit level. We are trying to see what the cockpit as a whole (i.e., all of its agents—be they human or machine) has to do at a given point to achieve the Mission Level Tasks (in the current situation). Then, what the cockpit as a whole has to do is distributed between the agents available in the cockpit.
It should be noted that the broader ATM system level is not explicitly referred to in the architecture. This is because the other aircraft are not under the control of the architecture. They are part of the context in which the architecture is flying (i.e. achieving its control tasks).
As indicated in Figure 3 below, several technical components have been specified, linking to the overall architecture concept.
A-PiMod Architecture (Source: Cahill et al. [2]).
As outlined in Table 4 below, the A-PiMod architecture consists of seven components and two separate software systems [22].
Component | 1. | Situation determination @Mission Level |
2. | Risk assessment @Mission Level | |
3. | Situation modification@ Mission Level | |
4. | Task determination @Cockpit Level | |
5. | Situation determination @Cockpit Level | |
6. | Task distribution @Cockpit level | |
7. | Risk assessment @ Cockpit Level | |
Separate Software Systems | 1. | Crew state inference |
2. | HMMI interaction manager |
A-Pimod architecture.
A key feature of the architecture is the distinction between components and modules:
A component is a functional unit that performs specific tasks (e.g. risk assessment at the mission level). In the A-PiMod architecture a component is always a small cooperative system made of the crew +1 module.
A module is a software system that acts with the crew as a team in this specific functional unit. The module provides assistance to the crew in the performing the component’s tasks
Thus regarding risk assessment at the mission level, it will always be performed together by the crew and the dedicated module, acting as a small team for that purpose. Both the crew and the module have the common goal to achieve the function assigned to the component. This is what brings great flexibility to the architecture. It allows each component to be executed exclusively by the crew (‘manual’ mode), e.g. the crew solely assess the risks, by the module (‘automated’ mode), e.g. the module solely assess the risks and the crew has no say in that evaluation, or by both the crew and module (‘mixed’ mode), e.g. the module produces a risk assessment and the crew acknowledge or reject it. This is true for all seven components in the architecture. Each is made of the crew and a dedicated module. Given the crew can participate to all seven components, the crew superposes the seven functions associated with the individual components, and this is exactly what human pilots do. They perform all these operations permanently, without being aware of that dissociation between seven elementary functions. They are thus part of the situation assessed by the Situation Determination/Modification @Mission Level component. All these tasks are permanently revised, distributed and executed, based on the context.
In addition, visual analysis of pilots’ behaviour is recorded to infer human operator’s (pilot’s) mental state, stress level, and general workload. The following instruments/technologies are used to obtain information about the pilot’s behaviour:
Eye tracking
Gesture recognition
Head pose
Crew interaction with cockpit systems is simple and user friendly. In addition to traditional controls, pilots interact with the system using voice and touch. This interaction is tracked by the system (i.e. what tasks performing, level of fatigue, involvement in activity). This form of tracking is referred to as ‘crew state monitoring’ [1]. Crew feedback is provided via a new cockpit user interface (Mission and Cockpit Level Management Display—MCMD). This provides information about (1) the risk status of the operational situation (this includes an assessment of the status of joint crew/automation system) and (2) what to do (including the provision of best options/alternatives based on different ‘technical’ contributing factors such as fuel remaining, the weather status at alternate airports and so forth) [1, 2]. As noted previously, the crew can over-ride system proposals/decisions, except in certain critical situations such as crew incapacitation.
The A-PiMod Mission and Cockpit Management Display (MC-M Display) enables communication between the Flight Crew and the new adaptive automation technologies [1]. The MC-M Display supports both mission and cockpit management tasks. As depicted in Figure 4 below, the proposed MCMD features two related sub-displays—the mission and cockpit level displays [21]. In the cockpit level display, one can see the tasks assigned to the crew and to automation. The software allows the crew to change that distribution—both manually (crew) or automatically (automation). In keeping with concepts of authority/pilot control, all task distribution changes have to be approved by the crew before becoming active.
The MC-M Display (Source: Cahill et al. [1]).
During the A-PiMod project a multimodal system prototype was developed to explore possibilities of multimodal interaction in context of flight deck. From the perspective of human-machine interaction, the flight deck interfaces should be able to accommodate large number of diverse tasks while maintaining high level of efficiency, usability and uncompromised safety. In A-PiMod multimodal interaction was demonstrated through the Multi-Modal Navigation Display (ND) [1, 2].
A number of other systems are linked to the crew state estimation and crew task determination components, for the purpose of inferring (1) the pilot’s mental state, stress level and general workload, and (2) predicting potential errors, missed events and/or overlooked information. This includes eye tracking, gesture recognition and head pose tracking technology [1].
Crew state monitoring (that is, focussing the Pilots attention on their state along with that of their crew member—and on the current and future state of the aircraft) is an important function of the third pilot. If fatigued, pilots may either forget or omit to review all the safe options. The 3rd crew member (automation) will consider all safe options and prompt the crew in regard to possible options, to ensure that a safe decision is made. In this context, a key challenge is how to get the two human crew members to share their ‘current state’ with the 3rd crew member [1]. Such an exchange should be meaningful and informative but not self-incriminating in any post hoc analysis [1]. Normal human interactions can easily accommodate this. For example, such information might be imparted as part of pre-flight social interactions/conversation. However, this is hard to replicate in relation to human/machine (i.e. third pilot) interaction
The assessment of crew state goes beyond issues of workload. It concerns many factors including crew experience, flight hours, crew familiarity with the proposed route and departure/landing airports, training background and so forth. The crew state might be assessed as less optimal in situations when the two crew members are unfamiliar with the route. From an operational/Pilots perspective, the starting point for crew state monitoring is the crew briefing/flight planning. Depending on the crew situation, this might occur a week before the flight and/or at the time of the pre-flight, flight planning and briefing task. In addition, knowledge of the joint crew status is and any issues linked to this is required [1, 2]. Potentially, the crew might need to report their status/state before the flight commences [1, 2]
The means/basis by which crew state information (i.e. eye tracking data, gesture data, voice, and touch data) is used to assess the crew state needs to be carefully considered. The assessment of this data depends on what we already know about the crew (for example, location in roster and expected level of fatigue). For example, if the crew are not looking at the correct area of the screen, and/or are blinking their eyes a lot, it may not be a problem. In this case, the crew might be very familiar with the route and may be more or less fatigued (i.e. location in roster—first day or last day). However the system might interpret this behaviour differently if the crew are unfamiliar with the route, and on the last day of their roster (i.e. expect higher level of fatigue)
If A-PiMod is to become a trusted (and relied upon) 3rd crew member, it needs to interact with the crew in a manner consistent with ‘normal’ human-human interaction [1]. Potentially, to be fully integrated in the ‘team’, it needs to engage in some form of ‘social’ interaction, and not only technical interactions [1, 2]. This latter issue has not been explored in A-PiMod and requires further consideration
How emphatic a team member is automation? When and how can it articulate its concerns, and potentially, over-ride the crew member’s decisions? In most (but not all situations), the cockpit crew have the final authority and can veto automation. This approach reflects an ‘adaptable systems’ logic. However, there may be situations (identified by the crew monitoring technology) where it is necessary for automation to ‘take charge’ to ensure flight safety (for example, situations of crew incapacity). Accordingly, three different levels of crew state monitoring can be defined. For example, (1) passive support, (2) active support and (3) intervention/over-ride [1]. In this sense, A-PiMod represents an adaptive automation approach. Arguably, a fully adaptive automation approach requires modelling and assessment of both the crew and aircraft state [1, 2]. The A-PiMod system represents a significant advancement in relation to the modelling of crew state (i.e. touch, voice, gesture, use of multimodal displays and so forth). However, aircraft state modelling potentially necessitates integration with aircraft systems and wider ATM and ground systems. This has not been demonstrated in the A-PiMod project
It is proposed that the Third Pilot/A-PiMod system (1) reflects a mix of the logic associated with adaptable systems and adaptive automation, while (2) also providing something new (i.e. multimodal cockpit concept).
In relation to (1), we are
Providing a framework for crew-automation cooperation for all activities occurring in the cockpit involved in the completion of a flight
Extending concepts of assistance (as defined by adaptable systems), where the crew are in conceived to be in control all of the time
Utilising certain features of adaptive automation—that is, providing task support to the crew following an assessment of crew state (i.e. inferences about crew situation awareness and workload)
In general, this new automation concept is predicated on concepts of partnership. The crew and automation are in charge together. As reported previously, in principle, the crew are in charge (concepts of professionalism and responsibility). However, there will always be particular safety critical situations when automation can take charge (i.e. fully adaptive).
In terms of (2), a new multimodal cockpit concept has been advanced. This enables improved assessment of the crew state/workload and provides a means to provide task and decision support information to the crew. Further, it allows for touch and voice interaction with cockpit systems. Evidently, the application of multimodal in the cockpit is not new. However, the application of crew interaction data (i.e. crew voice and touch interaction in the cockpit), as an input to crew state monitoring is innovative.
The third pilot has different modes of operation. This includes (1) passive monitoring, (2) active monitoring and (3) over-ride. It is anticipated that (1) and (2) will be the most typical operational modes. That is, providing task and decision support to pilots based on an understanding/assessment of the crew state and specific workload requirements at a given point in time. In certain non-routine situations (3) will be required (i.e. fully adaptive). The partnership/third pilot concept is defined in relation to the combination of these three modes of operation. In the future, mode (3) might be become routine, whiles modes (1) and (2) might become less routine. Either way, the advancement of this concept will require considerable effort in relation to both development and certification [1].
The expected benefits/impact were validated through extensive field research including 27 COP sessions, Validation Cycle 1 (VC1), Validation Cycle 2 (VC2) and evaluation with stakeholders at a demonstration day event [1, 2]. Overall, validation activities indicate that the ‘Third Crew Member’ may yield many operational and safety benefits. As defined in Table 5 below, these benefits can be grouped at different levels (i.e. cockpit, task, process and ATM System).
No | Benefit | Benefit level |
---|---|---|
1 | Improved access to information—faster access | Cockpit |
2 | Improved mechanisms to interact with information (i.e. voice, touch and gesture) | |
3 | Provide variability in way of the automation control selection (i.e. via voice, touch and gesture) | |
4 | Better interaction between automation and Pilots—clear communication lines, clear understanding of who does what and, when/how transfer work between both | |
6 | Flexible and natural interaction with cockpit systems (MM) | Task |
7 | Decision support—how to proceed in safety critical situation and/or normal situation | |
8 | Provide task support to crew in relation to assessing the situation and deciding on a course of action | |
9 | Reduce workload in complex/high risk situations | |
10 | Pilots always know ‘what is going on’ (i.e. reliable situation awareness) | |
11 | Complementary ways in which to interact with information (i.e. MM interaction) | |
12 | Reduction in workload | |
13 | Improvement in information management—support information acquisition and information analysis | |
14 | Improvement in situation awareness | |
15 | Improvement in situation assessment | |
16 | Provision of new information (i.e. status of operation, status of automation and routing advice) | |
17 | Improvements in the elimination of and recovery from human error | |
18 | Support for error identification and broader TEM | |
19 | Error reduction | |
20 | Error mitigation | |
21 | Error avoidance—avoidance of safety critical situations | |
22 | Better interaction between automation and Pilots—clear communication lines, clear understanding of who does what and, when/how transfer work between both | |
23 | Completion of mission in accordance with the flight plan | Process |
24 | Safe landing | |
25 | Reduction in accident rate | |
26 | Help mitigate the consequences of survivable accidents | |
27 | Indirect support—flight punctuality | |
28 | Indirect support—management of delays | |
29 | Indirect support—more efficient flights (and allied impact on fuel consumption) | |
30 | Reduction in accident rate | System/ATM |
31 | Indirect support—flight punctuality | |
32 | Indirect support—management of delays | |
33 | Potential link to SESAR SWIM concept (i.e. sharing information across ATM network) | |
34 | Potential to extend to support single crew operations |
Operational and Safety Benefits.
It is anticipated that this third pilot approach will enhance flight safety, especially in abnormal situations. The third pilot will not remove human error. Rather, it will reduce it. This is largely attributable to improvements in error detection and error management. A cockpit that is designed with the A-PiMod approach in mind will extend automation capabilities in an adaptive way, to the extent necessary to support a safer flight. Potentially, such an adaptive automation approach might prevent many accidents. In this regard, we might consider the AF 447 accident [8]. As indicated in the accident analysis research with COP members and VC2 participants, all 19 participants indicated that A-PiMod would have played a significant role in preventing this accident.
As reported previously, it is expected that the A-PiMod concept might lead to a reduction in the probability of fatal accidents by 43% [1, 21].
The assessment of safety impact concerns what has been advanced from a conceptual perspective (i.e. A-PiMod concept), as opposed to the technological progress (set of tools developed in the A-PiMod project, which reflects a particular implementation of the concept) [1, 21]. It is these set of tools that were validated in simulator evaluations [1, 2, 4]. These tools can be viewed as a first technical instantiation of the A-PiMod system. The sophistication, scope and integration of the tools can be improved in future research and development [22]. Once implemented, the expected impact might be further assessed.
In the perfect world, A-PIMOD would provide an airline with the most experienced team (crew and automation), capable of dealing with any situation (routine, non-routine, safety critical), where skill level is constant, across all weather/routes/airports/time zones. The third Pilot helps avoid dramas. The Third Pilot/A-PiMod will not eliminate human error. Rather, it will reduce error (human error is a reality and errors will always happen), and help manage them if they occur. In this way, the third pilot approach can be conceptualised in relation to ‘Smart Pilot Assistance’.
A-PiMod will enable the automation system to account for pilots’ emotional and cognitive states. For example, normal, tired, stressed, overloaded and incapacitated. Together with a thoroughly designed adaptive multimodal cockpit, this new technology will significantly improve the safety of flight, especially in abnormal situations and during situations of crisis management.
It should be noted that the assessment of safety impact has been undertaken for the A-PiMod concept, rather than for its particular implementation as achieved in the A-PiMod project. The A-PiMod concept is an advanced adaptive automation concept for a multi-modal cockpit, wherein the automation is seen as a third pilot, and crew and automation continuously adapt to each other and the context, such that safety is maintained. In the course of the A-PiMod project a particular implementation of the concept was achieved by development of a set of tools, and these tools were used in validation experiments (i.e. validation cycle 1 and validation cycle 2), in a flight simulator context. This set of tools can be viewed as a first technical instantiation of the A-PiMod system, and the sophistication, scope and integration of the tools can be improved in future research and development.
The research received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under grant agreement N. 605141—Applying Pilot Models for Safety Aircraft (A-PiMod) Project. We would like to thank member of the A-PiMod Project Team for their collaboration in this research. Further, we would like to thanks our COP members (particularly, Paul Cullen, William Butler, Martin Duffy and Stephen Duffy) for their invaluable input. Critically, the emerging adaptive automation concept is predicated on extensive feedback in relation to flight crew experience with automation (and associated problems).
It is well accepted that inflammation in the peripheral organs can influence homeostasis and immune responses in the central nervous system (CNS) [1]. In common neuropsychiatric conditions such as schizophrenia and depression, evidence indicates that neuroinflammation plays a role in the disease pathogenesis [2]. Long-lasting effects of neuroinflammation in such neuropsychiatric conditions are implicated with altered innate immune responses in the absence of specific pathogens [2]. However, until recently, it is not well understood how innate immunity, which was thought to have no lasting memory unlike adaptive immunity, can exert prolonged actions on the CNS. The recent discovery of innate immune memory (trained immunity vs. tolerance) shed a light in a long postulated role of innate immunity in neuropsychiatric diseases [3, 4].
Since the existence of the immune system was recognized more than 50 years ago, the immune system has been thought to be comprised of two components, innate immunity and adaptive immunity. Innate immunity is the arm that mounts nonspecific, acute immune responses, by sensing microbial by-products called pathogen-associated molecular patterns (PAMPs) or by-products derived from tissue injuries called damage-associated molecular patterns (DAMPs) [5]. Signaling through PAMPs and DAMPs are thought to play a major role in plant immunity [6]. In animals, adaptive immunity is the arm that develops antigen (Ag)-specific responses. The development of Ag-specific responses requires lengthy processes including antigen (Ag) processing by Ag-presenting cells (APCs), Ag presentation to T and B cells, and TCR or immunoglobulin gene arrangements of T and B lymphocytes, respectively, which lead to the development of Ag-specific T and B cells and finally antibodies (Abs) [7]. Adaptive immunity effectively eliminates hazards from the body through Ag-specific cellular and humoral immune responses [7]. Adaptive immunity results in the development of long-lasting Ag-specific memory T/B cells [8]. In this way, the body retains immune memory against specific pathogens for a prolonged time. It is well known that individuals who have survived measles will retain measles-specific immune defense for life.
In contrast, immunology textbooks have long taught us that innate immunity does not have any lasting effects or memory, and it is mainly effective in containing infection until adaptive immunity takes over. Innate immunity has also been known to shape adaptive immunity through multiple mechanisms such as affecting actions of APCs, thereby indirectly modifying adaptive immune responses [7]. However, recent exciting research revealed that innate immunity can have its own memory, following an immune stimulus, and this depends on time, amount, and the kinds of stimuli through metabolic and epigenetic changes [3, 9]. More importantly, the stimuli that evoke innate immune memory are not restricted to microbes; nonpathogenic challenges such as stress and obesity are also found to cause innate immune memory [3, 10].
As described previously, despite the accumulating evidence, it was difficult to understand how innate immunity exerts lasting effects, in the absence of specific pathogens or other persistent environmental stimuli, in neuropsychiatric conditions. The recognition of innate immune memory (trained immunity vs. tolerance) has provided us new insights with regard to the role of innate immunity in physiological as well as pathogenic consequences in the brain. In this chapter, research efforts shaping a concept of innate immune memory (trained immunity vs. innate immune tolerance) will be discussed first. In the latter part of the chapter, a potential role of innate immune memory in neuropsychiatric conditions, especially in ASD, will be discussed.
The presence of innate immune memory was first suspected because of unexpected, nonspecific effects of vaccinations. This is best known for a Bacillus Calmette-Guérin (BCG) vaccine. Epidemiological studies and subsequent randomized trials showed that the BCG vaccination not only provided protection for tuberculosis but also protection against other pathogens, especially those causing respiratory infection, which resulted in a reduction in infant mortality greater than expected for reducing tuberculosis-associated mortality [11, 12]. Likewise, the measles vaccination resulted in a striking reduction in children’s mortality, which was again not to be explained by the reduction in mortality caused by measles [11]. These epidemiological observations were further explored by researchers in the Netherlands. They first demonstrated that innate immune memory does exist in animal models [13]. Namely, these researchers showed that BCG provided enhanced protection against Candida albicans through nonspecific adaptation of innate immunity, independent of lymphocytes [13]. They proposed to name this process of innate immune memory “trained immunity.” The following studies by the same group also revealed that such adaptive changes in innate immunity are present not only in monocyte-macrophage lineage cells but also in other innate immune cells such as natural killer (NK) cells [14] and progenitor cells of innate immune cells in the bone marrow [15, 16]. Further studies revealed the presence of trained immunity in humans [17, 18, 19]. It became clear that trained immunity is similar to plant immunity which does not develop Ag-specific immunity, but develops prolonged immune defense by metabolic and epigenetic modulation [20]. Mounting evidence has now repeatedly shown that trained immunity is Ag nonspecific; the second stimulus (DAMP or PAMP) causing innate immune activation can be different from the first stimulus [3].
Adaptive changes observed in “in vitro” models of trained immunity with β-glucan, a representative PAMP from Candida albicans, have been extensively studied. It was revealed that ß-glucan treatment induces activation of the dectin-1/Akt/PTEN/mTOR/HIF-1α signaling pathway in innate immune cells [21]. That is, β-glucan activates dectin-1 which recruits Akt, leading to activation of mammalian target of rapamycin (mTOR) with suppression of PTEN expression and phosphorylation of the tuberous sclerosis complex (TSC) [22]. Activation of this pathway switches cellular metabolism from oxidative phosphorylation (ATP synthesis) to glycolysis, thereby reducing basal cellular respiration and increasing in glucose consumption, resulting in higher production of lactate [21]. Such metabolic changes lead to the exportation of citrate to the cytoplasm for cholesterol synthesis and phospholipid synthesis [23, 24].
This metabolic shift described above results in the replenishment of the Krebs cycle by metabolization of glutamine into glutamate and α-keto-glutamate, leading to an accumulation of fumarate [23, 24]. Higher concentration of fumarate inhibits the KDM5 family of H3K4 demethylase that eventually leads to epigenetic reprogramming [23]. It has been reported that in the initial phase of trained immunity, lysine 27 of histone 3 (H3K27) is acetylated and lysine 4 of histone 3 (H3K4) is methylated rapidly [25]. Although H3K27Ac gradually returns to the baseline over time, H3K4me3 was found to remain elevated in the trained immunity [25]. Such epigenetic histone modification (accumulation of H3K4me3) is known to lead to the remodeling of the local chromatin into an open and accessible state, resulting in the facilitation of the loading of transcriptional machinery. The remaining accumulation of H3K4me3 on chromatin has been implicated in the establishment of the epigenetic memory in the trained immunity [25, 26]. It was hypothesized that H3K4me3 increases the local hydrophobicity of the chromatin, allowing for liquid-liquid phase separated transcription factors to engage with the DNA in the aqueous environment of the nucleus, subsequently rendering loading of transcriptional machinery onto promoters [27, 28, 29]. This will allow cells to start rapid transcription of the genes necessary for immune responses, thereby causing a much stronger Ag nonspecific pro-inflammatory response.
Long noncoding RNAs (lncRNAs) can function as a molecular scaffold where multiple protein complexes can assemble, and they also guide these complexes to specific gene loci [30]. Recent research disclosed a new class of lncRNAs named immune gene-priming lncRNAs (IPLs), and IPLs were found to have a crucial role in the accumulation of H3K4me3 on chromatin [31]. A candidate IPL, termed upstream master lncRNA of the inflammatory chemokine locus (UMLILO), was found to be crucial for trained immunity; ablation of the UMLILO transcript abolished β-glucan-induced trained immunity in both human and murine monocytes [30].
As shown in epidemiological studies of vaccinations, trained immunity, caused by metabolic and epigenetic changes, will be beneficial in providing broader immune defense and promoting tissue repair [32]. On the other hand, maladapted trained immunity can be detrimental to human health. Chronic inflammatory conditions including neuropsychiatric conditions have been implicated with maladapted changes in trained immunity [2, 9]. It should also be noted that induction of trained immunity appears to be associated with doses of PAMP, perhaps DAMP in humans; depending on the dose and the kinds of PAMP/DAMP, tolerance can be induced, instead of trained immunity [2]. It has been shown that low to moderate doses of β-glucan, tri-DAP, and muramyl dipeptides are reported to induce trained immunity [33]. It also needs to be cautioned that the effects of trained immunity are likely associated with individual’s genetic and epigenetic background. For example, nonspecific effects of infant BCG vaccination are reported to be heterogeneous, affected by multiple genetic and environmental factors including age, gender, interactions with other vaccines, and exposure to infectious pathogens at the time of BCG vaccination [34].
It has been reported that pre-administration of pro-inflammatory innate cytokines [interleukin-1 (IL-1), tumor necrosis factor-α (TNF-α), and IL-6] provided protection against a variety of microbes [35]. Among the cytokines administered, IL-1 showed superior effects over TNF-α or IL-6 [35]. In BCG-vaccinated individuals, increase in production of these innate cytokines by monocytes in response to other microbes, other than BCG, was also found; this effect was again the most dependent on IL-1β [32]. IL-1β has also been reported to be crucial in the induction of trained immunity in NK cells [36]. On the other hand, in individuals with chronic mucocutaneous candidiasis, STAT-1-mediated type II interferon (IFN) induction was found to be crucial for induction of trained immunity [37]. The role of type II IFN (IFN-γ) in animal models was also reported by Kaufmann et al. [16]. However, in humans, innate immunity-associated protection (trained immunity) has been mainly implicated with IL-1β and other IL-1 families [38].
As detailed in the previous section, a metabolic shift from oxidative phosphorylation to aerobic glycolysis through the Hypoxia inducible factor-1α (HIF-1α) pathway downstream to mTOR is crucial for the development of trained immunity, since inhibition of this pathway is abolished induction of trained immunity [21]. Namely, in HIF-1α knockout mice, trained immunity was not induced [21]. IL-1β is known to be a direct target of HIF-1α [39], having a HIF-1α binding site in the promoter region of IL-1β gene [40]. It is now thus proposed that HIF-1α-induced IL-1β also plays a role in epigenetic changes, through histone modifications [35]. Alternatively, IL-1β has been shown to upregulate HIF-1α [41].
Given the role of IL-1β in trained immunity, excessive, dysregulated production of IL-1β is likely to cause maladapted trained immunity and resultant pathogenic consequences. This may be observed in patients with autoinflammatory syndromes associated with gene mutation that lead to overproduction of IL-1β, including cryopyrin associated periodic syndrome (CAPS) [38, 42]. On the other hand, impaired induction of trained immunity can also cause detrimental effects. It was reported that patients with chronic mucocutaneous candidiasis exhibit impaired induction of STAT-1-dependent, trained immunity in response to β-glucan [37].
The above-described metabolic shift is not limited to glucose metabolism. Changes in glutamine and cholesterol metabolism have also shown to be crucial in trained immunity [24]. Consequently, it is thought that increased cholesterol content also plays a role in the development of trained immunity. Interestingly, increased levels of oxidized low-density lipoprotein (OxLDL) caused by dysregulated cholesterol metabolism are found to induce trained immunity in human monocytes [10]. Such a finding indicates a pathogenic role for maladapted trained immunity in atherosclerosis, since monocyte and macrophage cells are known to play a major role in plaque formation in vascular endothelium, a major histologic change in atherosclerosis [10].
As detailed in the previous section, trained immunity causes a metabolic shift from oxidative phosphorylation (OXPHOS) to glycolysis, rendering macrophage and monocyte lineage cells to classically activated cells or M1 phenotype; these cells exhibit impaired OXPHOS and anabolic repurposing of the tricarboxylic acid (TCA) cycle [43, 44]. In contrast, alternatively activated or the M2 phenotype of macrophages and monocytes has balanced processes of OXPHOS and TCA cycle activation; enhanced glycolytic generation of pyruvate fuels the TCA cycle, paralleling the induction of OXPHOS [44]. Trained macrophages via ß-glucan exposure are shown to reveal M1 phenotype [21]. Generation of M1 vs. M2 phenotypes of macrophages indicates the importance of regulating innate immune responses for prevention of excessive, potentially harmful inflammatory responses. In addition to generation of M2 phenotype, hypo-responsiveness of innate immunity has been described as endotoxin tolerance and compensatory anti-inflammatory response syndrome (CARS) [45]. Such regulatory mechanisms also have lasting effects, as observed in trained immunity.
Endotoxin tolerance in innate immunity was first shown in rodent models of sepsis. Namely, survival from sepsis is associated with diminished or absence of responses to LPS, an endotoxin [46]. Subsequently, it was shown that previous exposure to a sublethal dose of LPS led to resistance to a lethal dose of LPS in rodents [46]. Endotoxin tolerance is thought to be a result of innate immune memory with lasting immune hypo-responsiveness, even to non-LPS stimulants [47]. Phenotypic changes of tolerant innate immune cells are characterized with less production of inflammatory cytokines (TNF-α, IL-12, IL-6) and increase in production of counter-regulatory cytokines (IL-10 and TGF-β) upon stimulation [48, 49]. CARS was recognized as a clinical syndrome which is thought to represent a phase of immune “exhaustion,” following initial potent immune activation, known as systemic inflammatory response syndrome (SIRS) [50]. Peripheral blood monocytes and neutrophils from CARS patients are reported to reveal similar phenotype to endotoxin-tolerant cells observed in rodent models [45, 49]. Recent research revealed that persistent effects of endotoxin tolerance and CARS are mediated by lncRNAs as well as microRNAs (miRNAs).
LPS activates TLR4 which leads to the activation of the myeloid differentiation factor 88 (MyD88)-mediated pathway and the TIR-domain-containing adaptor-inducing interferon-β (TRIF) pathway [45]. The molecular signature of endotoxin tolerance involves downregulation of TLR4, decreased recruitment of MyD88 or TRIF to TLR4, decreased activation of IL-1 receptor-associated kinase (IRAK)1 and IRAK4, diminished nuclear factor κ chain of B-cell (NF-κB) signaling, as well as upregulation of negative regulatory molecules including SH2 domain-containing inositol phosphatase 1 (SHIP1) [51].
Recent research revealed a role of miRNAs in the regulation of endotoxin tolerance. Specifically, miR-155 and miR-146α have been shown to regulate endotoxin tolerance [52]. MiR-146α reduces TLR signaling, by targeting IRAK1 and TRAF6, key components of TLR signaling pathway [53]. In contrast, miR-155 is reported to inhibit expression of SHIP1 and SOCS1, negative regulators of TLR signaling, prohibiting or attenuating tolerance induction by endotoxin [54, 55]. Several other miRNAs are also implicated with regulation of endotoxin intolerance [45]. It was shown recently that miR-221/miR-222 regulates functional reprogramming of macrophages during LPS-induced tolerization [47]. miR-221/miR-222 targets brahma-regulated gene 1 (Brg1), rendering transcriptional silencing of a subset of inflammatory genes that depend on SWI/SNF and STAT-mediated chromatin remodeling [47].
Recent research also revealed a role of lncRNAs in endotoxin tolerance; lncRNAs exert transcriptional, posttranscriptional, and translational regulation of gene expression [56, 57, 58]. Multiple lncRNAs are reported to regulate target molecules of TLR4 signaling pathways. LPS-responsive lncRNAs Mirt2, THRIl, MALTAT1, NKILA, lincRNA-21, and SeT have been reported to suppress expression of pro-inflammatory mediators including TNF-α [45]. For example, Mirt2 is reported to inhibit TRAF6 ubiquitination, leading to a decrease in TNF-α production [59]. However, at this time, relationships between actions of miRNAs and lncRNA in innate immune tolerance are not well understood. Other soluble mediators such as cytokines (IL-1β, IL-10, TGF-β, and TNF-α) are also reported to induce cross-tolerance or cytokine-mediated tolerance, causing a signaling cascade similar to that observed in TLR signaling [60]. In contrast, interferons (IFN-γ, α2-IFN, etc.) are known to abrogate endotoxin tolerance [61, 62]. Again these soluble mediators exert their actions on endotoxin tolerance via modulation of intracellular lncRNAs [45].
This type of innate immune memory (tolerance) is thought to be important in maintaining brain homeostasis, and impaired tolerance of innate immunity has been suspected in chronic neurodegenerative conditions such as Alzheimer’s disease [9]. Aging is associated with an increased load of gram-negative bacteria in the GI tract and mouth mucosa, resulting in an increase in endotoxin levels in the blood and the brain [62]. However, aging individuals tolerate higher LPS levels in the brain through developing endotoxin tolerance [63].
It is known that innate immunity does exist in the brain, playing a crucial role in brain morphogenesis and homeostasis. The major innate immune cells in the central nervous system (CNS) are microglial cells which are endogenously generated in the brain, but they can also be developed from bone marrow-derived monocytes, which are called BM-derived microglial cells (BMDM) [64, 65]. BMDM-induced inflammation has been implicated in neuropsychiatric conditions [64, 65]. It has also been reported that peripherally derived macrophages modulate microglial function after CNS injury; in this case, they are reported to exert anti-inflammatory effects [66]. Other innate immune cells in the CNS such as astrocytes are also known to exert important physiological roles [9, 67].
Inflammation in the periphery can prompt immune responses in the brain [1, 4]. Given the effects of trained immunity (activation vs. tolerance) in rodent models and humans, the development of maladapted innate immune memory in the CNS is expected to result in undesired, hazardous effects to the brain. However, reports concerning the effects of trained immunity and/or innate immune tolerance in the brain have been limited. Nevertheless, it was shown that microglial cells isolated from adult rats that were exposed to E. coli during the newborn period had increased expression of IL-1β mRNA [68]. The rats exposed to E. coli as newborns were also found to have impaired memory when they were challenged with a low dose of LPS, which was blocked by minocycline [2]. In experiments employing microglial cells obtained from sheep fetuses whose mother was given LPS intravenously, these fetal microglial cells were shown to have metabolic and epigenetic modulation, as has been reported in trained immunity [69].
Independent of the studies concerning trained immunity in the brain, persistent effects of maternal immune activation (MIA) on fetuses have been extensively studied, as one of the best studied rodent models of ASD [70]. In this model, sterile inflammation in pregnant rodents was induced with the use of PAMPs such as LPS, poly I:C, resulting in impaired neuropsychiatric symptoms in offspring in their adult years [70]. That is, offspring of MIA mothers have been shown to suffer from persistent behavioral symptoms and cognitive deficits frequently seen in ASD subjects later in life [70]. In addition, MIA also causes persistent alteration of adaptive immunity [71]. However, in this model, it is not yet well understood how innate immune memory (most likely trained immunity in this model) plays a role in a MIA model, causing persistent behavioral changes and impaired cognitive development. Children exposed to stressful events during the fetal and newborn period have also been reported to have higher levels of pro-inflammatory cytokines and neurodevelopmental impairment than control children [2]. Given the research findings in molecular mechanisms of trained immunity described in the previous section, there is a possibility that maladapted trained immunity contributes to the onset and progress of some neuropsychiatric disorders.
Tolerized innate immunity in the brain is thought to be crucial for limiting excessive inflammatory responses during brain tissue repair that involves phagocytosis of apoptotic cells and damaged tissue debris by tolerant phagocytes [72]. In rodent models, disruption of this pathway leads to neuroinflammation and subsequent neuronal damage [73]. An important regulator of this pathway is the triggering receptor expressed on myeloid cells 2 (TREM-2), which is expressed on microglial cells [74]. Blockade of TREM-2 was shown to exacerbate experimental autoimmune encephalitis (EAE), a rodent model of multiple sclerosis (MS) [75]. Apolipoprotein E (ApoE) which is a TREM-2 ligand was shown to have a role in maintaining tolerized phenotype of phagocytic cells [74]. This interaction was found to be impaired in patients with Alzheimer’s disease [9]. In animal models of Alzheimer’s disease treated with trained immunity vs. tolerance inducing stimuli, it was reported that long-term modulation of brain immune responses were observed, and the authors attributed this prolonged effects on innate immune memory to reprogramming of microglial cells [4].
In the previous section describing molecular pathways associated with trained immunity, the importance of mTOR signaling has been repeatedly shown. One thing we learned from the research on trained immunity is that multiple lineage cells reveal metabolic and epigenetic reprogramming in the process of innate immune memory, which, in animal models, can also be applied to microglial cells [4]. Interestingly, brain dysfunction caused by dysregulated mTOR signaling has been implicated in several neuropsychiatric disorders. In the next paragraph, we summarize mTOR-related brain dysfunctions and proposed mechanisms.
One of the expected consequences of excessive mTOR signaling caused by trained immunity is the impairment of lysosomal degradation of intracellular components, since mTOR activation inhibits autophagy via inhibition of the early steps of autophagosome biogenesis [76, 77]. Autophagy is a key physiological cellular function that clears intracellular molecules and thought to be developed to adjust the state of nutrient depletion [76, 77]. However, this is also an important mechanism to remove misfolded proteins that naturally occur in living cells [22]. In addition to degradation of misfolded proteins, autophagy also degrades altered subcellular organelles, such as the mitochondria [22]. Prolonged dysfunction in autophagy can lead to detrimental effects and is implicated in the pathogenesis of multiple neuropsychiatric conditions including dementia, movement disorders, seizures, brain ischemia, ASD, affective disorder, and schizophrenia [78, 79, 80, 81, 82]. In rodent models of depression, tuberous sclerosis, and ASD, rapamycin (sirolimus), a representative mTOR inhibitor, has been shown to attenuate social interactions and reverse behavioral effects on their neuropsychiatric symptoms [83, 84, 85, 86]. Thus metabolic and epigenetic changes caused by trained immunity may have profound effects through altered levels of autophagy, as a result of metabolic and epigenetic reprograming, as detailed in the previous section.
In this section, we discuss a possible role for trained immunity in the onset and progress of ASD. As a clinician, the author observed that an apparent strong immune stimulus altered the responses to subsequent immune stimuli in some, but not all ASD children and these ASD children also exhibit fluctuating neuropsychiatric symptoms, following microbial infection [87, 88]. As discussed in the previous section, in the MIA model of ASD, prolonged effects of MIA on the offspring brain can be explained through a concept of trained immunity occurring to the fetus at the time of sterile immune activation in the mother. This may have also happened in ASD subjects as described above. However, it should be noted that ASD is a behaviorally defined syndrome, diagnosed on the basis of behavioral symptoms, except for a minority of ASD cases that have well-defined gene mutations [89]. Therefore, based on the author’s clinical experience, it is likely that trained immunity plays a role in a subset of ASD subjects for whom neuroinflammation is associated in their ASD pathogenesis.
In ASD patients, just like in other neuropsychiatric conditions, a role of inflammation has been long suspected, and more and more evidence has been accumulating [90, 91, 92]. In the research of innate immune abnormalities in ASD children, we have also found evidence of dysregulated innate immune responses, shifting to pro-inflammatory responses in a subset of ASD subjects [88, 93, 94]. We also experienced that these ASD subjects suffer from various comorbid medical conditions involving the gastrointestinal (GI) tract and other organs [87]. Retrospectively, our findings may be reflecting maladapted innate immunity as a form of trained immunity in such ASD subjects; these ASD subjects may fall into an ASD subset which we have called inflammatory autism, mimicking the rodent ASD model of MIA [93]. Our previous findings that may indicate altered innate immune memory in such ASD patients are as follows:
In some but not all the ASD subjects, we found significant changes in innate immune abnormalities which are best reflected in changes in IL-1β/IL-10 ratios produced by purified peripheral blood monocytes (PBMo) [88, 93]. Namely, some patients reveal high ratios of IL-1β/IL-10, while others showed low ratios, and these rations can change from time to time, depending on their exposure to immune insults [93].
ASD subjects who revealed high and/or low IL-1β/IL-10 ratios also revealed fluctuating behavioral symptoms following immune insults [94]. Parents of these subjects often describe more severe, prolonged illnesses and frequent respiratory infection following microbial infection [87]. They also seem to reveal significant changes in their behavioral symptoms and cognitive activity with immune stimuli not associated with microbial infection; these ASD children may exhibit worsening neuropsychiatric symptoms, following flare-ups of aeroallergen allergy, delayed-type food allergy, and adverse reactions to medications including vaccinations [87, 94].
ASD subjects who revealed high and/or low IL-1β/IL-10 ratios also revealed changes in production of inflammatory monocyte cytokines including TNF-α and IL-6 [93, 95].
PBMo from ASD subjects who revealed altered IL-1β/IL-10 ratios also revealed changes in miRNA expression by PBMo, as compared to cells obtained from neurotypical, non-ASD controls [93].
We also studied changes in mitochondrial respiration in peripheral blood mononuclear cells (PBMCs) obtained from ASD subjects and non-ASD controls. Our results revealed evidence of altered mitochondrial respiration in association with changes in IL-1β/IL-10 ratios by PBMo in ASD subjects [95].
In recent studies, we also found changes in miRNA in sera of ASD subjects, when tested by high-throughput deep sequencing. Again, changes in serum miRNA levels are closely associated with changes in IL-1β/IL-10 ratios by PBMo, production of monocyte cytokines (TNF-β, IL-6, IL-10, CCL2 mostly), along with parameters of mitochondrial respiration (manuscript submitted for publication). Interestingly, in ASD subjects, miRNA levels are mostly decreased, as compared to non-ASD controls (submitted for publication). Targeted genes by miRNAs that are altered in serum levels in ASD subjects with high or low IL-1β/IL-10 ratios are associated with pathways involved in innate immune responses, including the mTOR signaling pathway (unpublished observation).
The above-described findings may be best explained by altered innate immune responses associated with innate immune memory (trained immunity vs. tolerance). So, if this is the case, for these ASD subjects, can clinical features that indicate an alternation of innate immune memory be detected? The author is a pediatric immunologist and, as indicated before, as stated previously, observes exacerbation of neuropsychiatric symptoms, following immune insults. Herein, a representative ASD case, in which trained immunity may be associated with the onset and progression of ASD, is presented.
A 10-year-old female child presented to the pediatric allergy/immunology clinic at our institution secondary to fluctuating behavioral symptoms. Fluctuation of behavioral symptoms often occurred, following microbial infection.
The patient was born at 41 weeks of gestation via cesarean section due to breech presentation, following an uneventful pregnancy. The patient was developing typically until 24 months of age and then suffered from significant developmental regression. Prior to the onset of the developmental regression, parents took the patient to South Asia to visit other family members and friends. During this visit, the patient suffered an insect bite which was complicated by a secondary bacterial skin infection. When treated with oral antibiotics abroad, the patient developed generalized hives and severe GI symptoms (nausea, vomiting, diarrhea, and bloating): the patient then became intolerant to multiple foods. After returning to the United States, the patient was given multiple vaccinations including live vaccines to catch up the vaccination schedule. All these vaccines were given while the patient was still suffering from GI symptoms and an active skin infection. Within several days after vaccinations (multiple vaccines given all together), noticeable loss of cognitive and motor skills became apparent in the patient. The patient was eventually diagnosed with ASD around 2.5 years of age.
Eventually, the patient’s GI symptoms subsided, but this subject never regained the cognitive skills that this patient had once acquired prior to the onset of developmental regression. Prior to advancing to pre-kindergarten, the patient was given booster doses of vaccines which were well tolerated. However, after starting pre-kindergarten, the patient started getting sick frequently with upper respiratory infections, which often evolved into ear infection. The patient missed many days of school, since the patient suffered a prolonged course of illness and more severe symptoms, as compared to peers. While the patient presented with symptoms of upper respiratory infection, this patient’s behavioral symptoms continue to fluctuate, most evident in worsening of obsessive compulsive behaviors and frequency of “rage” episodes. Worsening behavioral symptoms would always follow immune insults, worse in a convalescence stage. Avoidance of sick contacts by placing the patient in home schooling attenuated the fluctuating behavioral symptoms. At 7–8 years of age, the fluctuating behavioral symptoms seen were mainly associated with teething. After the completion of teething, behavioral symptoms became more stable. However, the patient stopped growing, falling under the first percentile of the growth curve in height and weight. An exhausting workup for primary mitochondrial diseases, endocrine diseases, primary immunodeficiency with known gene mutations, and congenital metabolic and genetic diseases was unrevealing. However, video electric encephalogram revealed a focal epileptic activity. Family history is negative for neuropsychiatric, genetic, autoimmune, immune, and metabolic diseases.
In the case presented above, did neuroinflammation caused by maladapted trained immunity have a role in her clinical features? It is hard to prove, but it may be speculated that the initial stressful events that occurred abroad shaped trained immunity in this patient, and the subsequent multiple unrelated immune stimuli may have caused prolonged maladapted trained immunity, leading to persistent neuroinflammation and impairment of cognitive activity, as observed in the MIA models of ASD. Interestingly, changes in GI conditions, such as changes in microbiome, have been implicated with neuropsychiatric diseases, triggering maladapted trained immunity [96]. It is also reported that trained innate immunity can be induced in human monocytes by cow’s milk [97]. Thus her severe GI symptoms and subsequent intolerance to multiple foods may be associated with excessive trained immunity in the gut of this patient.
As summarized in the previous section, we have found that IL-1β/IL-10 ratios produced by PBMo are altered in some ASD subjects in association with fluctuating behavioral symptoms [94]. Thus if innate immune memory (trained immunity) is associated with her above-described remarkable clinical symptoms, we may also find altered IL-1β/IL-10 ratios, as an indicator of altered innate immune responses.
Thus we assessed IL-1β/IL-10 ratios produced by PBMo in response to a panel of innate immune stimuli, including β-glucan, as reported previously [95]. As shown in Figure 1, the presented case revealed increase in IL-1β/IL-10 ratios in response to zymosan, CL097, and β-glucan. High IL-1β/IL-10 ratio in response to CL097, an agonist of TLR7/TLR8, was especially striking. We also observed increase in production of TNF-α and IL-6 and decrease in the production of IL-10, as well. Given these findings, it is possible that maladapted trained immunity may have caused excessive inflammatory responses to various innate immune stimuli, which then led to developmental regression and fluctuating behavioral symptoms in this presented case.
IL-1β/IL-10 ratios produced by purified peripheral blood monocytes in response to medium only (no stimulus), LPS (TLR4 agonist), zymosan (TLR2/TLR6 agonist), CL097 (TLR7/TLR8 agonist), and β-glucan in the presented case (patient) and control cells from a non-ASD neurotypical subject. IL-1β/IL-10 ratios are shown in a log scale.
Our deepening knowledge of innate immune memory (trained immunity vs. tolerance) has shed light on the understanding of nonspecific effects of microbial infection and other immune stimuli, which have been implicated in the onset and progress of various neuropsychiatric diseases. Recent research indicates a possibility for a role of maladapted innate immune memory in various neuropsychiatric conditions. The finding of innate immune memory is especially exciting in the field of neuroimmunology, since we now likely have better tools for addressing the long-suspected role of immune-mediated inflammation that is not associated with specific pathogens or environmental factors, in various neuropsychiatric conditions. The concept of innate immune memory will be especially important in addressing insults to the brain during the early years of CNS development, and the resultant lasting intellectual disabilities, as seen in MIA models [70]. More importantly, an improved understanding of the role of innate immune memory (trained immunity vs. tolerance) in pathogenic neuroinflammation can lead to novel therapeutic measures that are desperately needed for the treatment of neuropsychiatric diseases.
The part of the study presented in this manuscript was funded from the Jonty Foundation, St. Paul, MN. The author is thankful for the critical review of this manuscript by Dr. L. Huguenin.
The author has nothing to declare.
Ab | antibody |
Ag | antigen |
APC | Ag-presenting cells |
ApoE | apolipoprotein E |
ASD | autism spectrum disorders |
BCG | Bacillus Calmette-Guérin |
Brg1 | brahma-regulated gene 1 |
BMDM | cell, bone marrow-derived microglial cell |
CAPS | cryopyrin-associated periodic syndrome |
CARS | compensatory anti-inflammatory response syndrome |
CNS | central nervous system |
DAMPs | damage-associated molecular patterns |
EAE | experimental autoimmune encephalitis |
GI | gastrointestinal |
HIF-1α | hypoxia inducible factor-1α |
IFN | interferon |
IL | interleukin |
IPLs | immune gene-priming lncRNAs |
lncRNAs | long noncoding RNAs |
IRAK | interleukin-1 receptor-associated kinase |
LPS | lipopolysaccharide |
MIA | maternal immune activation |
MS | multiple sclerosis |
MyD88 | myeloid differentiation factor 88 |
mTOR | mammalian target of rapamycin |
NF-κB | nuclear factor of κ chain of B cells |
NK | natural killer |
OxLDL | oxidized low-density lipoprotein |
OXPHOS | oxidative phosphorylation |
PAMPs | pathogen-associated molecular patterns |
PBMCs | peripheral blood mononuclear cells |
PBMo | peripheral blood monocytes |
SHIP1 | SH2 domain-containing inositol phosphatase 1 |
SPUH | Saint Peter’s University Hospital |
TCA | tricarboxylic acid |
TLR | Toll-like receptor |
TNF | tumor necrosis factor |
TREM-2 | triggering receptor expressed on myeloid cells 2 |
TRIF | TIR-domain-containing adaptor-inducing interferon-ß |
TSC | tuberous sclerosis complex |
UMLILO | upstream master lncRNAs of the inflammatory chemokine locus |
"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges".
\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.
",metaTitle:"About Open Access",metaDescription:"Open access contributes to scientific excellence and integrity. It opens up research results to wider analysis. It allows research results to be reused for new discoveries. And it enables the multi-disciplinary research that is needed to solve global 21st century problems. Open access connects science with society. It allows the public to engage with research. To go behind the headlines. And look at the scientific evidence. And it enables policy makers to draw on innovative solutions to societal challenges.\n\nCarlos Moedas, the European Commissioner for Research Science and Innovation at the STM Annual Frankfurt Conference, October 2016.",metaKeywords:null,canonicalURL:"about-open-access",contentRaw:'[{"type":"htmlEditorComponent","content":"The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\\n\\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
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\\n\\nDigital Archiving Policy
\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. 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