Dysregulation of the dopamine system is central to many models of the pathophysiology of psychosis in schizophrenia. However, emerging evidence suggests that this dysregulation is driven by the disruption of upstream circuits that provide afferent control of midbrain dopamine neurons. Furthermore, stress can profoundly disrupt this regulatory circuit, particularly when it is presented at critical vulnerable prepubertal time points. This review will discuss the dopamine system and the circuits that regulate it, focusing on the hippocampus, medial prefrontal cortex, thalamic nuclei, and medial septum, and the impact of stress. A greater understanding of the regulation of the dopamine system and its disruption in schizophrenia may provide a more complete neurobiological framework to interpret clinical findings and develop novel treatments. Midbrng drugs , 11 thatng drugs –14, but Clinical imaging studies have provided strong support for the DA hypothesis of schizophrenia. Imaging studies that measured radioligand displacement from DA receptors as a measure of DA activity have shown that patients with schizophrenia display increased DA release in response to low-dose amphetamine, compared to healthy controls –17, whicMidbrain DA neurons can be subdivided with respect to their location, projection target, and functional significance , 37. TheDA neurons exhibit two patterns of activity, known as tonic and phasic states, that have different functional implications and are regulated by distinct afferent systems. In vitro in the absence of inputs, DA neurons maintain a basal activity state through the generation of a pacemaker conductance –45. HoweSpontaneously active DA neurons, in vivo, can display an irregular tonic firing pattern and rapid, phasic burst firing , 49, 50.Elevated DA system activity in schizophrenia results from dysfunction in a larger hippocampal-midbrain-striatal circuit, with a primary locus of pathophysiology that appears to develop in the vHipp. Deficits in the structure and function of the hippocampus are consistently observed in imaging and post-mortem studies of schizophrenia patients . ImagingOne can induce an analogous disruption of hippocampal physiology in animal models based on developmental disruption, including the methylazoxymethanol acetate (MAM) neurodevelopmental rat model , 77. TheSeveral brain regions can enhance VTA DA system activity through interactions with the vHipp-NAc-VP pathway. Here we discuss evidence indicating the involvement of the medial prefrontal cortex (mPFC), thalamic nuclei, and medial septum on the VTA DA system and how changes in the activity of these regions may lead to a hyperdopaminergic state as seen in schizophrenia.Dysfunction within the mPFC plays a central role in the pathophysiology of several psychiatric illnesses, including schizophrenia. For instance, contrary to the increased presynaptic striatal DA synthesis and release , it has The mPFC is thought to be a major regulator of the DA system but with the outcome, either inhibitory and excitatory responses, reflecting the specific anatomy of mPFC afferents to the VTA. Two major mPFC subdivisions, the infralimbic (ilPFC) and the prelimbic (plPFC) cortices, send direct projections to the VTA  as well Whereas vHipp activation upregulates DA responsivity, the amygdala decreases tonic DA neuron firing. Activation of the BLA has been shown decrease DA neuron population activity in the medial affect-related regions of the rat VTA, which is proposed to be due to a glutamatergic projection to the ventral pallidum, because blocking glutamate in the ventral pallidum prevents BLA activation-dependent down-regulation of DA neuron firing . FurtherIt is worth noting that the mPFC does not project directly to the vHipp . Thus, tThe thalamus has long been implicated as a potential node of dysfunction in schizophrenia  mainly dRecent rodent studies have similarly indicated circuit abnormalities underlying and resulting from thalamic dysfunction . The thaWe showed that activation of the nucleus reuniens increases DA neuron population activity in the VTA via its projection to the vHipp, since it was prevented by vHipp inactivation . Also, aAnother thalamic nucleus recently implicated in the regulation of the VTA DA system is the paraventricular nucleus of the thalamus. It was observed that the pharmacological activation of the paraventricular nucleus of the thalamus enhanced VTA DA neuron population activity, which was completely prevented by the inactivation of either vHipp or NAc . Both thAnother brain region that may influence vHipp activity and, in turn, regulate midbrain DA system activity is the medial septum. The medial septum sends dense cholinergic and GABAergic projections to several hippocampal regions , 124, inThe GABAergic projections from the medial septum synapse primarily on PV+ interneurons in the hippocampus , 129, whA receptors on interneurons in the vHipp. This would inhibit interneurons, which in turn leads to the disinhibition of pyramidal neurons. On the other hand, in MAM rats, GABA released in the vHipp induced by the medial septum activation would be more likely to reach pyramidal neurons due to the loss of interneurons in the vHipp. These changes combined with the loss of cholinergic activation of a parallel set of GABAergic projections to vHipp pyramidal neurons that impact the substantia nigra  and Sao Paulo Research Foundation (FAPESP Young Investigators Grant - 2018/17597-3 to FG).AAG has received consulting fees from Alkermes, Lundbeck, Takeda, Roche, Lyra, Concert, and research funding from Lundbeck.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Recently, collagen therapy has been made available for treating rotator cuff tendon injuries. However, to our knowledge, there are no clinical studies objectively investigating the effect of collagen therapy.A 53-year-old female patient visited our pain clinic because of pain in the right shoulder. Although she had never experienced trauma and had not overused her shoulder and arm, the patient showed limited range of motion with painful arc syndrome. Moreover, the Neer test and Hawkins–Kennedy test were positive with subacromial tenderness.The MRI findings revealed partial tears on the articular surface of the anterior supraspinatus tendon in the rotator cuff.The patient was treated with injections of exogenous collagen at the site of the partial tear under ultrasound guidance.Follow-up MRI after injection of collagen revealed healing of the previous partial rupture of the tendon without any complications. Moreover, the patient reported reduction in pain and improvement in the movement of her shoulder during the follow-up period.In this report, we demonstrate healing of a partial tear of the supraspinatus tendon in the rotator cuff after injection of exogenous collagen, as confirmed by MRI. However, currently, there are no clinical studies objectively investigating the effect of collagen therapy. To our knowledge, this is the case report confirming the recovery of the supraspinatus tendon through type I collagen therapy using magnetic resonance imaging (MRI).Injury of the rotator cuff tendon is one of the most common causes of pain and disability in the shoulder. There are several non-surgical treatments for this condition. Recently, type I collagen  has become physically available for the treatment of rotator cuff tendon injury.2A 53-year-old female  visited our pain clinic with complaints of pain in the right shoulder and upper arm, lasting 1 month, with a numerical rating scale (NRS) intensity of 6. According to her medical history, she had never experienced trauma and had not performed any movements requiring overuse of her shoulder or arm. On physical examination, she had limited range of motion of the right shoulder due to the pain, which indicated painful arc syndrome. In addition, the Neer test and Hawkins–Kennedy test were positive with subacromial tenderness. Furthermore, she underwent MRI for the right shoulder prior to the initiation of treatment. The MRI result showed partial tears on the articular surface of the anterior supraspinatus tendon in the rotator cuff . Repetition of the aforementioned therapy did not lead to improvement in her symptoms.Therefore, we decided to inject type I collagen  into the partial tear of the supraspinatus tendon. After preparing the skin to prevent infection, we scanned the partial tear of her supraspinatus tendon under ultrasound guidance. Subsequently, we injected type I collagen (1 ml) mixed with 1 ml of 1% lidocaine into the location of the tear Fig. . Type I Finally, MRI of the right shoulder was performed 2 months after the first injection of type I collagen. The results confirmed the healing of the previous partial thickness tear in the articular side of the supraspinatus tendon Fig. . Moreove3This is the case report of treatment with exogenous type I collagen injection, for which healing of the lesion at the site of the partial tear of the supraspinatus tendon in the shoulder was confirmed by MRI. Shoulder pain is exacerbated by persisting compression of the subacromial space due to weakness of structures, including the tendons and rotator cuff muscles.,2 Occasionally, the tendons of rotator cuff muscles become irritated and inflamed, leading to compression of the subacromial bursa.Rotator cuff tear is one of the most common causes of pain, accounting for 40% to 60% of all cases of shoulder pain. Conversely, it is more effective to directly inject steroids at the site of infection without other organ injury. Nevertheless, frequent injection of steroids also inhibits the healing process, leaving the tears of tendons at an inflamed status.,5 In this case, we had started the treatment with the suprascapular nerve blocks followed by subacromial and subdeltoid bursa injections twice with steroid combining medications; nonetheless, the pain did not resolve. Therefore, we considered another method to treat the tear of tendon, while avoiding surgery.A rotator cuff tear can be treated surgically or through conservative therapies . However, there are several limitations of both modalities. The administration of medicine is a simple approach to pain relief. Nonsteroidal anti-inflammatory drugs relieve pain and swelling caused by the response of the bodys immune system to inflammation in the tendons. However, the long-term use of these medications is associated with complications, including gastrointestinal problems, elevated cardiovascular risk, and liver damage.,7 In the rotator cuff tendon ECM, type I collagen constitutes >95% of the total collagen, whereas the remaining 5% consists of collagen types III and V.,9Collagen is a biomaterial which is helpful in wound healing and remodeling. There are several types of collagen  in fibrous tissues, such as tendons, ligaments, and skin. Type I collagen, the major component of the tendon extracellular matrix (ECM) in all connective tissues, affects the structural and mechanical properties of tendon tissues.–12 In addition, the ability to synthesize collagen decreases after 60 to 70 years of age, which increases the susceptibility to degenerative disease. Moreover, the decreasing tendency in the ratio of type I collagen to type III collagen is a major cause of ECM disorganization.Regarding the reasons for the effectiveness of collagen therapy, we expect that disorganization of the tendon ECM worsens the shoulder structure due to changes in collagen composition caused by inflammation. Injection of exogenous collagen into the lesion exerts a therapeutic effect by stimulating the restoration of fiber and induction of collagen synthesis. We injected an exogenous collagen, termed atelocollagen, which binds to the already existing collagen in the tissue; use of this collagen is not linked to intolerance, allergies, or infections. Administration of collagen at the target site activates the integrin receptors in fibroblast cell membranes. Consequently, the growth factor cascade initiates the synthesis of endogenous collagen. Finally, this pathway heals the damaged collagen fibers and leads to proper alignment.–19Therefore, restoration of the tendon ECM through injection of type I collagen could heal the tear of structures. The effect is based on the induction of regenerative pathways by the synthesis of endogenous collagen and the reorganization of collagen fibers in damaged tendons. In addition, treatment with arthroscopic application of collagen for rotator cuff repair was effective. However, these studies were conducted using collagen patches rather than injection of exogenous collagen, which may have led to different therapeutic results.Several studies have demonstrated that type I collagen plays a major role in the proliferation of the ECM. It has been reported that type I collagen enhances the healing of rotator cuff tendon in a rabbit model using a collagen patch at the site of the tear. Despite the availability of several animal and intraoperative studies on the clinical effects of collagen patches, there are insufficient investigations to objectively establish the effectiveness of exogenous collagen injection. Therefore, this case report showing tendon recovery after injection with atelocollagen, as confirmed by MRI, is clinically meaningful.Thus far, the effectiveness of collagen therapy was assessed by monitoring the patients symptoms after injection. that spontaneous healing occurs has not been substantiated by histological examination. In a series of 35 en-bloc histological sections from surgical specimens of partial tears, Fukuda et al did not observe active repair in any of the examined portions.–27 Hamada et al reported that, similar to any other tendon of the body, there is a potential for repair of torn tendons of the rotator cuff; nevertheless, its ability for effective closure of the defect is debatable. From the clinical and histological aspects, spontaneous healing of partial tears is unlikely, except on rare occasions. Various untoward factors involved in the repair of a torn tendon include aging, separation of the tear caused by contraction and the weight of the arm, hypovascularity, shear stress within the tendon, and subacromial impingement.We cannot exclude the possibility that the patients partial tear of the supraspinatus tendon resolved spontaneously rather than through the injection of atelocollagen. However, there is limited knowledge regarding the natural history and spontaneous healing of partial tears. Codmans assertionIn conclusion, collagen therapy has been applied to rotator cuff tear. However, there have been no objective results regarding the use of exogenous type I collagen injection. In this case report, using MRI, we confirmed healing of the lesion at the site of partial tear of the supraspinatus tendon after injection of type I collagen.Conceptualization: Won-joong Kim.Data curation: Won-joong Kim.Formal analysis: Won-joong Kim.Funding acquisition: Won-joong Kim.Investigation: Won-joong Kim.Methodology: Won-joong Kim.Project administration: Won-joong Kim.Resources: Won-joong Kim.Software: Won-joong Kim.Supervision: Won-joong Kim, Heeseung Lee.Validation: Won-joong Kim.Visualization: Won-joong Kim.Writing – original draft: youbin Jo.Writing – review & editing: youbin Jo, Won-joong Kim.


The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands.Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations.Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters.In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications.Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians.• Implementation of AI in radiology is facilitated by the presence of a local champion.• Evidence on the clinical added value of AI in radiology is needed for successful implementation.• The online version of this article (10.1007/s00330-020-06946-y) contains supplementary material, which is available to authorized users. Artificial intelligence (AI) is increasingly being recognized as an important application in clinical radiology –5. RecenAI is expected to cause large changes in clinical work practices and requires complementary skills from radiologists , 7, 8. ATechnology implementation in hospital settings involves a large variety of stakeholders and organizational procedures, with strong routines and professional identities, as well as strict legal and regulatory standards –20. ConsIn this study, we use the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework  to identWe used an embedded multiple case study approach, investigating seven Dutch hospitals. 1Using a maximum variability logic, interviewees occupying different positions within the participating hospitals were selected  improved diagnostic practice due to more precise and objective diagnoses, avoidance of mistakes, and the automation of cumbersome tasks; and (2) operational benefits, such as diminishing workloads, time-saving, more consistent reporting across radiologists, and advanced service availability.Second, although there is little empirical evidence, radiologists, members of hospital management, and technology developers easily integrated into existing IT systems used by radiologists, such as picture archiving and communication systems (PACSs). This means that the output of the AI application should be displayed with the least possible clicks. Also, AI applications should be implemented without large changes to routines and workflow practices, i.e., by avoiding additional steps for reporting the result of the AI application. For example, the interviewed users experienced the integration of BoneXpert into the PACS as very smooth, being the main reason for its perceived user-friendliness. However, concerns remain about the integration of other AI applications into the PACSs.Third, in order for AI applications to be perceived as user-friendly by radiologists, they need to be innovation strategies. In four of the seven cases, a hospital-wide innovation strategy including AI was present, reflecting innovation leadership among the hospital management. Such leadership is also manifested by the presence or absence of a designated innovation manager (present in three of the seven cases). On radiology department-level, only one hospital had a formalized innovation strategy regarding AI. However, four more hospitals were developing such a strategic approach at the time of this research.Fourth, the openness towards AI application in radiology is expressed by the adoption of hospital-wide or radiology department–specific local champions are vital in initiating and stimulating implementation within their department and taking the lead during the entire process. These local champions are radiologists that show a particularly strong interest in AI applications and usually have better than average understanding of the technical aspects of AI applications. To overcome the opposition of potential skeptical colleagues, local champions appear to follow two strategies: (1) providing general information on AI or on an AI application in particular through scientific articles, books, and presentations; and (2) promoting opportunities for experimentation with an application, e.g., by organizing showcases or installing a test version of the application. Both strategies aim to build trust and serve to familiarize and convince other radiologists (direct adopters) and referring clinicians (indirect adopters) with the AI application.Fifth, interviewees and document analysis , 29 showknowledge-exchange platform among its members facilitating the implementation of AI applications. The NVvR has included AI in its strategic research agenda since 2017 [Finally, the Radiological Society of The Netherlands (NVvR) serves as a nce 2017  and has Table planning and monitoring of AI implementation tend to be unstructured. From an organizational perspective, clinical benefits or organizational goals that might be achieved by using AI applications are not clearly established ex-ante and therefore hard to assess after implementation. From a workflow perspective, implementation plans do not specify how the AI application should be integrated into the clinical workflow, which leads to significant variations in the way the application is used in different departments. Furthermore, in all cases, the work done to monitor existing practices or the impact of the implementation of novel technologies on the level of the hospital is currently limited. The unstructured nature of implementation processes can be explained by the absence of official guidelines or best practices.Second, a lack of empirical evidence on the effect of AI applications on the radiological workflow, as well as their added value for clinical radiology practice. One reason is that measuring clinical and organizational benefits of AI on a micro-level is difficult. There is, for example, no standard methodology to measure increases in the quality of diagnosis. When evidence on the technical performance is available (such as with BoneXpert), interviewees noted that publications on the validation of the algorithms are based on laboratory rather than clinical settings.Third, there is funding for AI applications is uncertain due to the lacking evidence on the added value of AI applications needed to back adoption and funding decisions. Moreover, the benefits and costs of using AI may be unequally divided over departments, which complicates funding decisions. If other departments are to cover parts of the additional costs, they need to learn about the technology and the potential benefits of using AI applications. This requires efficient communication between departments, a shortcoming in several of the studied cases.Fourth, acceptance and trust of direct (radiologists) and indirect adopters (referring clinicians) in AI applications differ greatly. Radiologists’ perspectives on AI applications range from outright enthusiasm to curiosity, skepticism, and fear [Fifth, the and fear . These dand fear . An impoAchieving acceptance of the referring clinicians is important since they are the potential final “customers” of the AI applications’ output. Interestingly, in three hospitals, we found that the referring clinicians did not trust the output of the AI application and redid a manual bone age analysis for every scan. Thus, just like the radiologists, the referring clinicians showed varying levels of acceptance of AI applications.regulatory and legal uncertainties for AI applications in radiology [Finally, lacking jurisprudence from the European General Data Protection Regulation and the new Medical Device Regulation (MDR), which will come into effect in May 2020, leads to several adiology , 33. Curadiology .Based on the empirical findings, the NASSS framework was adapted and refined for the case of the implementation of AI applications in radiology. The main adaptation of the original NASSS model to form the NASSS for AI in radiology concerns the “adopter system” Fig. . It is eThis research contributes to the existing empirical evidence on the implementation challenges of AI-based medical technologies. We identified lacking acceptance as one of the most important causes for non-adoption, abandonment, and thus a barrier to the successful implementation of AI applications in radiology. The determinants of radiologists’ acceptance of AI application found in this study are in line with evidence from surveys among radiologists , 35 and Another implementation challenge we found, the role of evidence on innovation implementation, has been discussed extensively in the field of evidence-based healthcare . ScientiAI applications in radiology are predicted to not only support but also potentially automate certain medical decision processes, thereby calling into question the jobs of highly educated individuals. This element of job displacement due to automatization adds to the complexity of adoption and implementation processes in the field of health digitization. While the possibility of AI replacing radiologists, and thereby threatening their professional identity, was extensively mentioned in many recent radiology publications , 16, 44,Due to its exploratory nature and qualitative empirical approach, several limitations of the research need to be taken into consideration. This research only focuses on AI applications in Dutch radiology departments and could not be generalized to other healthcare systems. Across the cases, individuals with different roles and positions were interviewed, limiting the generalizability of the results to other hospitals in The Netherlands and beyond. Interviewees varied with regard to their related experience and knowledge, due to the early stage of implementation of AI in radiology practice. Therefore, the sample of interviewees is possibly biased towards individuals with a particular interest and above-average positive attitude towards AI applications.In order to achieve better generalizability of the results, further research should investigate applications that present higher technical complexity than BoneXpert and represent a larger part of the diagnostic work done by radiologists. Furthermore, it is important to understand how country-specific political and social contexts determine the implementation processes. Future studies can identify specific technical challenges for the implementation of AI applications, e.g., datasets and associated requirements . Additionally, future research should focus on the impact of the implementation of AI applications on the knowledge development of radiologists.Considering the great attention AI applications are receiving in radiology and other medical disciplines like pathology, understanding the barriers of and facilitators for the implementation of AI is important. One of the important facilitating factors is the presence of a “local champion,” an individual with a strong personal interest in AI applications who most often initiates and actively advances AI implementation in the organization. Among the most prominent hindering factors is the uncertain added value for clinical practice, which causes low acceptance of AI applications among adopters and complicates the mobilization of funds to acquire AI applications. Furthermore, the failure to include all relevant stakeholders in the planning, execution, and monitoring phase of the implementation of AI applications was found to be a major hindering factor. To increase the acceptance among adopters, more evidence of the added benefit of their AI applications in the clinical setting is needed. Also, all involved stakeholders (most notably radiologists and referring clinicians) should be included in the decisions for and the design of implementation processes of AI applications.ESM 1(DOCX 24 kb)


The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. The Internet of Things (IoT) consists of low-cost efficient sensors, actuators, and computing units and provides great benefits to people to synthesize a system of interrelated computing, sensing, and communication devices that facilitates and improves everyday life, in cities and industry. IoT is foreseen to reach 500 billion devices that are connected to the Internet by 2030 , while tThe success of the computation offloading, and consequently the performance of IoT-enabled applications, depends on many contextual parameters, e.g., the user’s mobility, various wireless parameters, and the resource availability of the computing resources in the data center. Most of the modern IoT-enabled applications rely on continuously moving people or mobile agents. Regarding the latter, various types of autonomous mobile agents or unmanned vehicles are used. Typical examples of these agents are the unmanned aerial vehicles (UAV), which are widely used in several human activities in the context of smart city, agriculture, area surveillance, rescue missions, and event coverage . UAVs caThe computation offloading aims to save time and energy at the end user’s side. Cloud computing seems the natural selection for offloading, as it is the prevalent service delivery model nowadays. However, the high network delay for sending data over public internet counterbalances the benefits of the powerful computing resources that are available at a cloud data center. Accordingly, Multi-Access Edge Computing (MEC)  and Fog -Workload Profile: The IoT applications generate time-varying traffic in terms of request size and number of flows. Additionally, the involved wireless communication between the mobile devices and the back-end software components introduces additional uncertainties that considerably affect the offloading decision and the resource scheduling at the edge computing infrastructure. Contrarily to existing average traffic characteristics, building dynamic traffic profiles and prediction mechanisms will enable more accurate, adaptive, and successful data offloading and resource allocation mechanisms.-Performance Modelling: Most of the existing models for describing the performance of IoT-enabled applications are empirical and usually focus on a specific performance metric, e.g., response time, throughput, energy consumption, etc. These models cannot adequately capture the dynamic nature of the emerging applications, which in turn leads to either performance degradation or resource over-provisioning. On the other hand, the DRUID-NET framework proposes formal dynamic multi-input multi-output performance models applicable to various IoT applications. These types of models enable the design of novel controllers for finer regulation of Quality of Service (QoS) metrics.-Resource Allocation: Usually, most studies in the literature combine a static performance model with solving an optimization problem. However, this approach assumes that the workload does not vary significantly, which limits their validity, applicability, and exploitability. In contrast to these approaches, we envisage to design stabilizing controllers in order to guarantee the feasibility of the resource scheduling and the performance requirements.-Control co-design The DRUID-NET framework examines specifically the case where the application is the controller design and its implementation for dynamic processes. In this setting, the performance of the closed-loop system is considered in the overall application performance. The control co-design approach aims to design feedback mechanisms achieving closed-loop system properties such as reachability, stabilization, and other complex specifications, and simultaneously design and implement resource allocation algorithms for the dynamic network.This article presents the vision and perspective of the DRUID-NET  framework, along with a detailed description of its main concepts and objectives. While considering the end-user’s mobility and the parameters of the wireless connection environment, the DRUID-NET framework aims at developing workload profile holistic and modular dynamic performance models of IoT-enabled applications based on the appropriate theoretical tools. Furthermore, the article aims to outline the control principles of novel resource management systems for these kinds of applications. In particular, the key research threads and topics of this article are summarized as follows:The rest of the article is organized as follows. In This section provides a thorough yet comprehensive presentation of the most relative studies to the DRUID-NET framework, in the recent literature. Aligned with the DRUID-NET objectives, Abdelzaher et al.  presenteThe estimation of the workload and communication patterns in IoT-Fog/Edge networks has only been explored a little due the high heterogeneity of co-existing devices. Nevertheless, there is no doubt that the proper estimation of the offered workload and communication patterns could lead to a more efficient utilization of the underlying infrastructure.Authors in  considerA promising approach to derive workload profile is to use machine learning techniques. Applying deep or machine learning techniques for IoT applications is not new , but mosResource allocation has become one of the most important open research problems in Cloud and Edge computing and IoT. In the cloud computing environment, the computing resources are assumed to be infinite; thus, static or empirical models combined with coarse resource scheduling techniques have been shown sufficient to provide high performance through over-provisioning. However, these approaches are neither optimal nor able to provide QoS guarantees. Regarding application’s performance modeling, the empirical or fixed models considered already known request sizes and execution times, which are not only hardware-specific, but generally very difficult to be precisely computed. Furthermore, many studies relied on queuing models , e.g., GContrarily to cloud computing, the resources of edge computing are rather limited; thus, static allocation techniques cannot achieve optimal resource utilization. Furthermore, modern time- and mission-critical IoT-enabled applications ,32 have In the 5G era, Network Functions Virtualization (NFV) and Software Defined Networks (SDN) play key roles for the realization of many type of verticals, which are comprised of several IoT applications. In this context, virtualized and isolated Service Chains (SCs) comprised of a series of Virtualized Network Functions (VNFs) implemented as VMs need to be deployed in the available MEC infrastructure to offer networking services to the IoT traffic. Normally, the objective of this kind of resource allocation mechanism aims to minimize the overall deployment cost  . AnotherIn control theory, the effect of a shared, imperfect communication network between the controller and the sensor/actuator network has been studied extensively for almost three decades, generating the separate branch of Networked Control Systems (NCS), Ref. ,48. NCS entropy of the system [Several methods have addressed these non-idealities separately. Time delays, for instance, have been tackled utilizing perturbation theory, Lyapunov stability theory, and hybrid systems analysis, but also probabilistic methods involving Markov chains and stochastic automata . Quantize system . From the system ,52,53. De system . Here, te system .Additionally to the stochastic results , recent Rather than designing the control and communication protocol in two steps, co-design methods aim to synthesize simultaneously controllers and the communication patterns . Applied only to networked control systems with constrained communication resources so far, co-design methods have been extensively studied the last decade ,61,62,63Nevertheless, there is limited work on the co-design of controllers taking into account simultaneously more than one phenomena  caused by the distribution of computing and communication resources. It is anticipated that the research developments in the upcoming decades will allow for encapsulating, comparing, and subsequently altering the impact of the several non-idealities, and this in turn will have a significant impact on future control applications, where resources must be used parsimoniously, in balance with the constraints and the overall considered objective. This will require and motivate new paradigms in Systems and Controls, where multi-objective optimization, model-free (data-driven) approaches, approximate optimality (however with firm safety guarantees), reconfigurability, and resilience take a central place.As mentioned before, a major challenge for solving the resource allocation problem in edge computing settings is to predict the time-varying characteristics of the workload/traffic, as different traffic flows and volatile conditions can influence significantly the resource allocation mechanism. Aspects such as the load generated from an IoT device, latency specifications, the transmitting data frequency, the wireless protocol, the mobility of the devices, and the number of devices associated with the IoT gateway, change the amount of resources requested from the edge, while also influencing the scheduling process. Until now, only generic traffic models have been proposed to estimate the traffic aggregated at the edge layer, while stationary IoT devices are assumed, leading to a static rate model, which however limits its effectiveness and applicability in real scenarios.Going a step beyond from the pertinent literature, which only considers average and general traffic characteristics of the IoT applications , DRUID-NET framework aims to differentiate and categorize the requirements of various IoT applications using appropriate data analytic and mathematical models. In particular, we classify and categorize the IoT applications by leveraging the transmission patterns, the spatial and temporal correlation of the traffic, as well as other traffic related characteristics such as the frame size distribution, and the burstiness of the traffic of the IoT applications. The novelty of this approach is that we create prediction mechanisms to treat the dynamics and uncertainty in the corresponding traffic profiles. Each predictive mechanism targets specific categories of IoT applications with similar requirements and characteristics to define the type, the size, as well as the time and the location of the requested resources. Furthermore, with this approach, we can dispose the erroneous assumption that specific tasks are associated with static and pre-specified resource footprints. In contrast, we replace this analogy with an opportunistic association between the requested resources and the IoT traffic dynamicity, thus introducing a holistic mechanism inspired by data analytics, and traffic analysis methods.A first classification of the IoT applications can be produced by simply answering yes or no, to questions regarding the involved “things”/devices. Indicative such questions can be identified as follows: (i) Are the devices heterogeneous? (ii) Are they battery-powered? (iii) Are they sending data with high or low frequency? (iv) Are they data rich ? (v) Are the devices mobile?The answer to such questions will help us to create a first clustering of the IoT applications. These clusters will contain IoT applications with similar device characteristics and behavior. Nonetheless, this first-phase categorization does not necessarily mean that the IoT applications belonging in the same cluster will present the same exactly resource requirements at the Edge. The reason is that different network access technologies can significantly affect the network requirements of the IoT applications. For example, different access technologies  have different characteristics in terms of packet length, transmission range supported, MAC mechanisms, topological characteristics of the associated IoT devices , number of device connections supported, etc.Thus, DRUID-NET takes into consideration both the functional and network requirements of the IoT applications in order to provide a complete and realistic IoT application classification.The above categorization will help us to extract the workload generated from each cluster of applications in terms of bandwidth, latency, and other important Key performance Indicators (KPIs) during the offloading of IoT tasks to the Edge. Specifically, through this approach, we can propose appropriate mathematical models to simulate the traffic behavior of the various IoT applications. Nonetheless, even with this modeling, a lot of ambiguity will exist. The reason is that IoT access networks include several uncertainties, usually being wireless, lossy, and unreliable. Hence, the goal of DRUID-NET is not only to categorize and classify IoT applications based on their traffic profiling, but to also apply network analytics to make the communication as deterministic as possible.Our goal is to replace the so far average estimates of the IoT applications with instantaneous and accurate transmission metrics. To this end, appropriate machine learning algorithms  need to be integrated in the traffic profiling in order to learn and predict the network conditions between the IoT and the Edge. This can be decisive in the performance of the subsequent resource allocation at the Edge. The Edge controller will be able to adapt to and predict the changing workload arriving at the Edge infrastructure, creating a holistic and realistic resource allocation approach.The available resource models are usually single-input single-output. Energy or response time are typically the model’s outputs, while computing resources , incoming requests, and network bandwidth are the control variables. In most of the current studies, the relation between input and output is fixed and empirically derived. For example, the processing time of a request is proportional to its file size and inversely proportional of the service rate measured in CPU cycles or millions of instructions per second. Although this assumption is reasonable, the actual processing time depends on several time-varying parameters, which are not easily measured. Furthermore, in combination with static resource allocation mechanisms, the offloading decision performs adequately only for specific operating conditions, being unable to guarantee stability under fluctuating workload and heterogeneous IoT communication infrastructure.Contrary to current approaches that provide empirical static models, we aim to develop formal, realistic, and dynamic traffic and resource models applicable to emulate the generated traffic from various IoT applications. For this purpose, DRUID-NET adopts hybrid dynamical models  that havThe workload profile estimator and the dynamic model of the resources and the overall status of the network/servers provide the foundation upon which the resource allocation algorithm will be developed. Specifically, the objective is to develop a joint communication, computing and storing virtualization paradigm that is updated and adapted dynamically. For this purpose, we consider the problem of simultaneously (i) allocating storage, computing and communication resources, (ii) modifying network topology/ protocol, and (iii) structuring the edge computing data centres (such as VM distribution). Two distinct approaches relating to static and dynamic resource allocation are considered.In this approach, we do not take into account the dynamic nature of the processes under study; however, we consider the full resource allocation problem. The method is oriented towards solving multi-objective optimization problems fast that will in turn provide the optimal operating point for the communication network, and the computing and storage allocation in the edge/cloud servers. Our goal is to describe the complex interrelations between the aforementioned resources in an analytic manner, merging available models, e.g., from queuing theory and Markov models. Next, we plan to solve the optimization problems using mixed-integer, linear, and nonlinear programming. Since the complexity of these problems does not allow often exact real-time solutions, our intention is to propose approximate solution algorithms that provide guarantees of the level of suboptimality of the identified solution. Additionally, we will employ machine learning algorithms to relax the complexity of these highly nonlinear/nonconvex problems so they can be solved in real-time, thus respecting hard time constraints. This approach will focus on problems involving complex specifications and mostly static models, aiming to maximize the QoS delivered.In this approach, the DRUID-NET framework proposes dynamic control-theoretic resource allocation mechanisms. Utilizing the models established by capturing the performance metrics dynamics in a hybrid dynamical system, our goal is to follow control-theoretic approaches that provide formal guarantees on important properties describing the resource allocation problem. For example, a main objective is to provide guarantees of the speed of convergence of the performance metrics to a pre-determined range, defined by translating the QoS requirements to mathematical statements. Moreover, our goal is to provide decision mechanisms that allow structural changes in some cases , together with continuous strategies (such as CPU and memory utilization in a server). The natural, main challenge in this approach is the scalability of the decision algorithm, which will be tackled by proposing smart allocation strategies that allow trade-offs between performance and real-time implementation. Another challenge is to establish resource allocation mechanisms using only partial information, which is the most realistic scenario. This issue will be addressed by proposing distributed control mechanisms that take continuously into account local information and receive only intermittently information about the states of the whole system.As we have mentioned before, in the broad field of Systems and Control, several different paradigms have emerged in the last few decades, to deal with the control of IoT-enabled cyber-physical systems. Indicative examples include hybrid behavior, quantized control, varying delays, safety-criticality, nonlinear control, etc. Although these challenges are typically met together in IoT environments, the research activities have led to disconnected communities, and likewise very specific and custom control techniques that limit their implementability in a holistic framework. In a real-life IoT control application, these non-idealities take place all together. We argue that the different paradigms separately introduced for each of these non-idealities are hard to reconcile, thus the DRUID-NET framework is devoted to deploying the theoretical results in actual applications.Modern IoT applications need controllers that address a mixture of these undesired phenomena. Our goal is to establish a formal decision mechanism that will be able to change the provisioning of the resources in real time, adapt its control objective to the available bandwidth, weigh the cost of communication with respect to the advantage of involving decentralized agents, and eventually address a multitude of practical challenges appearing in networked, resource-constrained control applications. Such a new generation of controllers will be made possible by the merging of two sets of hybrid models, namely (a) the performance model having as internal variables performance metrics of the infrastructure and as inputs the resource distribution and utilization, and (b) the process model . To provide an example of the challenges that will be met in this setting, let us raise the following question: what do traditional data-rate theorems from quantized control  become The proposed architecture is generic enough offering a holistic paradigm, while its estimation, modeling, and control methodologies are applicable in several categories of IoT applications, such as the ones based on mobile agents  or designed for crowded smart areas or emergency scenarios. The following subsections demonstrate three representative use cases of the DRUID-NET framework.Collaborative robotics is a prerequisite for Industry 4.0, especially in the Industrial Internet of Things setting. The current trend is to produce and program robots that have the capability to work together, or in close proximity, to humans in a shared environment. Removing a physical  cage from the robot brings many challenges, the most critical of which is guaranteeing safety/avoiding collision, without leading to an unsatisfactory performance, e.g., the robot working in a non-acceptable speed. The setting can be extended to the case where there are many robotic agents and humans sharing the factory floor, or any other indoor or outdoor environment, e.g., a logistics warehouse, an airport, swarm UAVs networks, etc. In all aforementioned cases, similar challenges appear, namely: (i) intermittent and noisy measurements of the position of the agents either by static sensors or sensors mounted on the robots, (ii) faulty wireless communication networks, (iii) stringent safety specifications as humans and robots move freely in the same environment, and (iv) time-critical specifications. These challenges become harder when the computing/storing/communication resources are limited, or not always available to a control application, which is the typical case. Thus far, a few approaches, aligned with the ones appearing in the cyber-physical systems control problems, take explicitly into account a part of these challenges, e.g., . ControlThis scenario enables, and is enabled by, a combination of almost all components of the DRUID-NET framework, namely workload and resource estimation, and control co-design of a set of control applications in a platform where resources are shared and their availability is volatile.In the case of a physical disaster, the fixed communication infrastructure could be destroyed or unavailable due to high workload demand. Furthermore, for rescue operations, it could be critical to deploy additional on-demand computing and network resources at the proper place and time, in order to alleviate any remaining network infrastructure and collect data from remaining communicating devices such as mobile phones or sensors, towards helping to locate and rescue survivors. Mobile agents, especially UAVs, are suitable for these kinds of missions and can provide additional edge resources capable of processing the data at low latency and organizing the rescue operation. In order to serve the survivors devices as much as possible, there is a need to predict the kind and amount of resources these devices will request and the location of these resources. Some UAV-mounted edge resources may need to be deployed sporadically and temporarily at different locations based on IoT devices needs and mobility. Thus, there is a need to anticipate the deployment of edge services and to estimate the time they will be required at a given place to decide whether it is worth deploying durable edge resources, or instead mobile temporary resources could suffice. In this latter case, the estimation of the location and quantity of required resources should be anticipated to allow their timely deployment. The deployment of edge resources will be such that a maximum of IoT devices can be served within the required latency, either directly or through multi-hop communications. Direct communications will be favored for devices with very-low latency requirements, while multi-hop communications could be used for weaker latency requirements non-necessary communications. The trajectory of distributed UAV should be consciously planned accordingly, taking into consideration the time restrictions (robots should be deployed at the proper place before we need them).In this case, using swarms of mobile robots will assist in eliminating the non-essential communications. Combining service differentiation and smart data offloading to UAVs, there will be reduction of any unnecessary communication between users and overhead due to extensive signaling. Since the number of available robots may still be inadequate to serve all ground services, the prioritization of the applications, flows, and devices is of paramount importance for the success of critical missions. Under these circumstances, the priority will be given to the areas with many victims or of major importance for the completion of the mission; thus, the available swarms of mobile robots should be distributed accordingly. Even in the case of homogeneous UGVs and UAVs with identical computing and networking capabilities, the optimal allocation of UAVs or UGVs formulates a dynamic optimization problem, which depends on the size of the damaged area, the communication ranges of both UGVs and UAVs, the flying altitude of UAVs, the propagation conditions, the data communication requirements  and the number and type of devices to serve. For example, if the victims are equally spread in different locations, the robot swarms would be equally scattered to deploy their resources in these areas so that the maximum number of devices would be served directly. On the other hand, the mobile robots will be driven to the most damaged area in order to serve the required network traffic.This scenario illustrates the use and combination of the different control components of the DRUID-NET framework, and in particular: (1) workload estimation in quantity, time, and space, (2) resource allocation (tasks assignments to UAV and/or robots) and (3) path trajectory.Most of the modern smart city applications rely on mobile end-devices of continuously moving humans. Thus, the user’s mobility is a dominant parameter of IoT systems. As shown in This use case illustrates the necessity and the collaboration of the involved components of the DRUID-NET framework. Particularly, workload estimation, dynamic performance modeling, and resource allocation components interact to meet the respective requirements and optimize the resource utilization under varying workload conditions.This article presents the most important challenges of IoT-enabled applications, along with the perspective and the basic concepts and objectives of the novel DRUID-NET framework. The corresponding components of the DRUID-NET framework are carefully designed to address several emerging challenges at any level of the IoT/Edge/Cloud system, stemming from mobile end-devices up to powerful cloud data servers.In particular, the workload estimation aims to create a profile of IoT applications that includes features of the generated data, parameters of the wireless connection, and patterns of the user’s mobility. The performance modeling components identify the multi-input multi-output dynamical systems that capture the dynamic operation of the applications, and are utilized to design the resource allocation and the offloading decision strategies. The resource allocation component is in turn responsible for deciding any control action at any level of the hierarchical system. Depending on the objectives of the controller, the resource allocation can be either static or dynamic, providing guarantees on QoS metrics, e.g., response time or energy consumption, and system properties, such as stability. Finally, the co-design of the controllers enables the binding between the IoT application and the resource control algorithm in order to provide guarantees for the closed-loop system.The DRUID-NET framework aspires to verify its modular architecture and components through different IoT scenarios. These use cases are carefully selected in order to cover all main challenging and emerging aspects of the IoT applications and Edge computing paradigm.


In recent years, there is concern about the shortage of adult children who are active participants in the care of their older parents (those 65 years and above). Reviewed studies suggest that attitudes towards the provision of care for older parents differ and may depend on ecological affordances. Particularly, demographic changes due to urbanization shape constructions of obligation to provide eldercare. This study examined patterns of filial responsibility and felt obligation of adult children toward their older parents among rural and urban dwellers in the US. Participants (N= 187) responded to questionnaires assessing filial responsibility, felt obligation to parents, and additional social and demographic characteristics. Results show that rural Americans reported higher levels of filial responsibility and felt obligation to older parents. Also, filial responsibility is associated with the felt obligation for contact with parents, avoidance of conflict with parents, provision of assistance and care, self-sufficiency and independence from parents, and sharing of personal experiences with parents. Our findings indicate the existence of cultural ecological contexts that afford a sense of duty, care, and assistance to older parents. We discuss strategies that acknowledge the relevance of these factors in promoting participation in eldercare.


Numeracy is critically associated with personal and vocational life-prospects  and therefore contributes considerably to our understanding of numerical cognition from the neuro-cognitive foundations to the acquisition and teaching of numerical skills. In the following, we provide examples of research from several fields.Disciplinary fields such as the learning sciences, cognitive science, neuroscience, and educational psychology may overlap in terms of more basic or more applied research. For reasons of parsimony, we conceptualized The number of meta-analyses published since 2015 manifests the contributions from both more basic and more applied research. Examples of more applied research on numerical cognition include evaluations of effectiveness of interventions in early childhood . Implementation of explanatory models or interventions previously proven effective in basic research often fails to produce similar results in educational practice because, at each hierarchical level, humans make decisions which introduce new variables. While researchers are cognizant of some of these variables, and often consider these as noise with the aim to control these through experimental design or statistical models, this noise may be the key to the comprehension of use-inspired basic research.However, within the domain of educational practice, the Collaboration, whether across disciplines or within educational contexts, with the explicit aim of conducting use-inspired research, is not easy. Berliner claimed that education research “is the hardest science of all” Be flexible and sensitive to time and schedulesConsider that research that was effective in the lab may not be effective in the classroomAsk questions educators want and need to have answeredDisseminate findings in non-academic media ; attend educator specific conferences.https://osf.io/ndyb6/). Sharing preprints via social media, talking to educators face-to-face, going to educator focused conferences, or any other means of closing feedback loops are examples of ways to move research on numerical cognition forward within and across disciplines.Space limitations do not allow us to provide multiple successful examples of such use-inspired research (e.g., Hawes et al., More than 100 years ago, James  not onlyEach of the authors contributed equally to the theoretical framing of this article and contributed to edits throughout the writing process. HS wrote the initial draft based on notes from members of our workshop held at the Brazilian-German Numerical Cognition Winter School, Belo Horizonte, Brazil, August 2019. To acknowledge his work in transcribing our workshop notes, HS was considered the first author. HG located the call for manuscripts at Frontiers, managed the contributions of co-authors and finalized the manuscript. Therefore, we listed her as the second author.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The International Conference on Frailty and Sarcopenia Research Task Force met in March 2020, in the shadow of the COVID-19 pandemic, to discuss strategies for advancing the interdisciplinary field of geroscience. Geroscience explores biological mechanisms of aging as targets for intervention that may delay the physiological consequences of aging, maintain function, and prevent frailty and disability. Priorities for clinical practice and research include identifying and validating a range of biomarkers of the hallmarks of aging. Potential biomarkers discussed included markers of mitochondrial dysfunction, proteostasis, stem cell dysfunction, nutrient sensing, genomic instability, telomere dysfunction, cellular senescence, and epigenetic changes. The FRAILOMICS initiative is exploring many of these through various omics studies. Translating this knowledge into new therapies is being addressed by the U.S. National Institute on Aging Translational Gerontology Branch. Research gaps identified by the Task Force include the need for improved cellular and animal models as well as more reliable and sensitive measures. The International Conference on Frailty and Sarcopenia Research (ICFSR) Task Force met in Toulouse, France on March 10, 2020 to discuss geroscience. The timing could not have been more prescient: On the following day, March 11, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic  the discovery and quantification of the biological mechanisms of aging; 2) the study of aging and aging-related diseases in preclinical models, and; 3) the design and execution of clinical trials of testing interventions  to optimize the health and function or resilience of multiple physiological systems. Indeed, a multidisciplinary approach is necessary to expedite the translation of newly discovered therapies to clinical application.The WHO introduced the concept that healthy aging and disease prevention hinges on preventing declines in intrinsic capacity — a composite of physical and mental capacities that peak in early adulthood and tend to decline in later years. WHO developed a model for integrated care of older people (ICOPE) that focuses on maintaining intrinsic capacity through the adoption of a healthy lifestyle .As individuals age, they transition between robustness to frailty, defined as increased vulnerability to endogenous and exogenous stressors and a decline in physiological reserve and function across multiple organ systems. Thus, frailty and intrinsic capacity represent distinct but related concepts, both with similar physiological underpinnings . PhysiolGeroscience assumes that aging itself, defined as the accumulation of diverse forms of molecular and cellular damage and repair, ultimately drives the increased risk of chronic diseases among older people . López-OTogether, the biological mechanisms progressively result in loss of cellular homeostasis, dysregulation across multiple physiological systems and, ultimately, disease, disability, and death. Both the accumulation and the repair of aging hallmarks are strongly influenced by behavior, the environment, and genetics, resulting in substantial variation among individuals. This has led to the important concept that biological age differs from chronological age. Biological age, in contrast to chronological age, is difficult to quantify, which has led to inconsistent definitions in the literature. Herein, approaches to measure aging hallmarks are presented as a means to better define biological age of cells, tissues, and, ultimately, organisms.One of the longest longitudinal studies of normative human aging, the Baltimore Longitudinal Study of Aging (BLSA), has been running for more than 60 years. The BLSA collected multidomain clinical and functional data from participants with increasing frequency as they aged: every 4 years for those under age 60 increasing to every year for those over age 80. Using these data, the investigative team recently proposed a roadmap to build a phenotypic metric of aging, which by systematically characterizing the continuum of aging in an individual, could advance understanding of the kinetics of aging, as well as discovery and development of effective interventions. The framework encompasses four domains: body composition, energetics, homeostatic mechanisms, and neurodegeneration/neuroplasticity .Scientists in the NIA Intramural Research Program are also conducting a Study of Longitudinal Aging in Mice (SLAM) to better understand whether discoveries in mice can be translated to humans and which aging phenotypes share or do not share common traits in order to fine tune interventions to translatable outcomes. They are conducting the study in two strains of mice of both sexes, selecting the C57BL6/J and the UM-HET3 mice to better recapitulate both the genetic homogeneity of most aging studies and the heterogeneity found in humans respectively. SLAM investigators are conducting a broad range of clinically relevant assessments over time and across multiple domains. To assess frailty, they will apply two newly developed tools: the mouse clinical frailty index and mouse frailty phenotype assessment . For exaMetrics of aging span multiple domains and include biological hallmarks, organ impairments , functional limitations , and disease and deficit accumulation . Undoubtedly, as people age, the onset and progression of decline within each domain differs; and understanding whether biomarkers of these different domain are mechanistically connected and exploring temporal relationship between domains is critical to translate this science into effective interventions. Based on the geroscience premise that aging itself is the driver of the majority of chronic diseases and geriatric syndromes, quantifiable indicators or “biomarkers” of biological age would be of significant utility to target individuals who are experiencing accelerated aging as well tracking the effectiveness of interventions aimed at slowing down the aging process.For clinical research, biomarkers of aging could enable identification of persons appropriate for and, potentially, most responsive to interventions targeting the biology of aging. In such trials, biomarkers may be used to verify target engagement and also serve as informative surrogate endpoints that may change well before clinical outcomes . In cliTable Omics-based laboratory biomarkers have also been investigated in the FRAILOMIC initiative ,21, an iFRAILomic studies thus far have shown that biomarkers of frailty change according to clinical characteristics of participants, suggesting the existence of different clinical phenotypes of frailty. For example, omics biomarkers may be associated with disability, sarcopenia or other organ-specific diseases, and vary by sex, ethnicity, and race. Lab biomarkers appear to be modestly associated with classical biomarkers such as biomarkers of inflammation, hormonal changes, and glucose dysregulation , particuFor example, in one study of FRAILomic participants, serum levels of the soluble receptor for advanced glycation end-products (sRAGE) was shown to be an independent predictor of mortality in frail individuals, suggesting that sRAGE levels may be useful for prognostic assessment and treatment stratification . AnotherFuture studies of biomarkers of aging and their cross-sectional and longitudinal relationships with parameters of function  and resilience  affected by advancing age are warranted. Longitudinal studies promise to provide greater insights into rates of biological aging and, potentially, in the context of clinical trials, the extent to which the molecular and cellular effects of aging can be attenuated and/or reversed. As novel interventions targeting the biology of aging emerge, biomarkers of aging will facilitate their testing and development.The Translational Gerontology Branch (TGB) at the National Institute on Aging (NIA) is part of the NIA’s intramural research program. Research conducted in TGB labs ranges from drug discovery using a variety of in vivo and in vitro models to clinical and longitudinal studies. For example, TGB researchers are studying the cellular and molecular mechanisms underlying aging, diseases of aging, and longevity, including the hallmarks mentioned earlier. Clinical studies have explored domains of the aging phenotype such as changes in body composition, energy imbalance, homeostatic dysregulation, and neurodegeneration and the impact of those changes on disease susceptibility, reduced functional reserve, impaired stress response and healing capacity, impaired physical function, disability, and dementia. The NIA also supported establishment of the Translational Geroscience Network (TGN) to develop, implement, and test standard operating procedures for translational early phase trials of agents that target fundamental aging processes and to select, optimize, and validate ancillary measures of fundamental aging processes for use across all trials (R33 AG061456). TGN provides statistical and data management support and has established a biobanking and repository network.Healthy aging involves both delaying the physiological consequences of aging and maintaining functioning as aging progresses. Interventions thus need to focus on preventing frailty and disability. To develop effective and feasible interventions for healthy aging, whether drugs, exercise, diet, or combinations thereof, will require a deeper understanding of the mechanisms of aging as well as identifying biomarkers that track with biological, not simply chronological age, and predict when an individual is approaching a tipping point and nearing a threshold of irreversible decline.The pathway to these biomarkers is through the interdisciplinary field of geroscience, which seeks to define the biological mechanisms of aging that give rise to age-related diseases and disorders and to identify targets that may be amenable to different kinds of interventions . DevelopThe interdisciplinarity of geroscience will be essential to define the complex interactions of the multiple biological, physiological, and behavioral pathways that contribute to age-related declines in health. Interdisciplinarity will also ensure that advances in geroscience are applied to other biomedical disciplines such as neuroscience and cardiology.


A robust body of evidence points to the importance of views on aging (VoA) in influencing developmental outcomes in later life. Now that this connection has been well-established, research has turned to examine mechanisms, processes, and pathways by which VoA and health are interconnected throughout adult development. The presentations in this symposium use longitudinal datasets to address the pressing question of how VoA and health are associated over time. Timescales range from 6-months post-hospital stay, to 9 year longitudinal follow-up. Presentations will provide evidence concerning both physical and mental health conditions and symptoms . Furthermore, the studies focus on various aging outcomes, ranging from functional ability  to recovery from hospital stays , to sensory loss , to specific medical conditions including depression, back pain, rheumatism . Three of the talks provide evidence for the effects of VoA on health, and one presents evidence of specific health conditions as longitudinal predictors of later VoA. Thus, an important question that this cohesive set of talks will raise is the issue of bi-directionality, by which VoA affect health, and vice versa. Concluding the symposium, the discussant will share insights about how these presentations contribute to the current understanding of mechanisms of the associations between VoA and health, and the role of VoA for ageism. She will additionally provide an overview of new areas for future research that can help to advance the understanding of these complex interactions.


Durum wheat is one of the most important cultivated cereal crops, providing nutrients to humans and domestic animals. Durum breeding programs prioritize the improvement of its main agronomic traits; however, the majority of these traits involve complex characteristics with a quantitative inheritance . This can be solved with the use of genetic maps, new molecular markers, phenotyping data of segregating populations, and increased accessibility to sequences from next-generation sequencing (NGS) technologies. This allows for high-density genetic maps to be developed for localizing candidate loci within a few Kb in a complex genome, such as durum wheat. Here, we review the identified QTL, fine mapping, and cloning of QTL or candidate genes involved in the main traits regarding the quality and biotic and abiotic stresses of durum wheat. The current knowledge on the used molecular markers, sequence data, and how they changed the development of genetic maps and the characterization of QTL is summarized. A deeper understanding of the trait architecture useful in accelerating durum wheat breeding programs is envisioned. Crop production needs to satisfy future demands while facing extreme climate changes and hurdles in natural resources (such as water and soil) and disease management . The main challenge faced by plant scientists in the 21st century is to increase the crop productivity per unit area and, at the same time, enhance sustainability and preserve biodiversity . These have important implications on the breeding efforts and strategies for cereals, particularly wheat, which is a main source for food consumption worldwide.The United Nations expects the world’s population to grow from seven to nine billion by 2050 (Triticum aestivum L.) is cultivated globally, durum wheat (Triticum durum Desf.) represents about 5% of the total wheat production, with almost 17 million ha worldwide and a global production of 38 million tons in 2019 , and is primarily cultivated in three different regions: the Mediterranean basin, the Northern Plains between the United States and Canada, and the desert areas of the south west of the United States and Mexico [Although common wheat  markers , and polThe application of PCR led to the explosion of ESTs available in several plant databases, which have revolutionized current molecular biology and genetic approaches. In this revolutionary phase, DNA-based markers represent solid bases in plant breeding programs for their high rate of polymorphisms, numerous alleles for each locus, genome-wide distribution, and compliance to automation. However, the advances in high-throughput sequencing technologies provide new solutions for the development of high-density genetic maps, such as diversity array technology (DArT) , Genotyp® Wheat 660K SNP array, the Wheat 55K SNP array, the Axiom® HD Wheat genotyping (820K) array [Due to the high number of SNPs in genomes compared with other DNA-based markers, high-throughput SNP discovery technology has been used more commonly for fine mapping studies. In particular, high-throughput sequencing technologies gave rise to millions of SNP markers , allowinK) array , the WheK) array , and theK) array . These aK) array , in partAmong these arrays, the high-density (90K) wheat SNP array developed by Wang et al.  represenSNPs were widely used for the development of highly saturated genetic maps with 100,000 loci ,34 for gMolecular markers have been widely used for the construction of linkage maps, representing the position of molecular markers along the chromosomes measured in genetic distance, based on the recombination events between individuals. The different applications of linkage maps in plant breeding are crucial for the identification of the associations of molecular markers with traits of interest for gene discovery, comparative genomics between different species, and physical mapping to facilitate genome assembly. Linkage maps have been developed for most crop species, from fruit trees to vegetables and cereals.The first linkage map in durum wheat was developed by Blanco et al. . This mahttps://www.diversityarrays.com/; accessed on 24 July 2020). With these high-throughput platforms, Maccaferri et al. [Based on the high coverage reported utilizing the newly developed DArT markers, new consensus maps of durum wheat were developed by Marone et al.  and Macci et al.  construcThree consensus maps have been developed for durum wheat in the last decade. The consensus map developed by Marone et al.  integratDurum grains, a storehouse of nutritional elements for the human diet, contain starch (70.2%), proteins (12.2%), lipids (1.9%), fiber (1.6%), and minerals (1.6%), with varying water content . In addiThe kernel quality is of paramount importance in the end-product quality of commercial wheat varieties, determining the type of products that can be produced. Thus, it is possible to distinguish a commercial value in the beginning of wheat production and a technological value linked to the worldwide market requirements for end-product uses.For the commercial value, the wheat quality is primarily evaluated through the milling rate, which consists of the quantity of wheat flour derived from 100 kg of seeds. This rate aims to obtain the maximum quantity of flour, analyzing the seed size, thousand-kernel weight, ash mass , and the percentage of seed defects, such as pre-germination, small or white seeds, and pathologic darkness of the grain. For the technological value, wheat quality is evaluated for the strength of the flour protein when mixed with water to make dough , the quantity of water required for workable dough, and the flour color . The protein quantity and quality influence the characteristics of the end wheat products: for example, a high protein content brings major water absorbance, increasing the productivity rate and the shelf life of the final products. Several investigations ,83,84,85Carotenoid pigments have an enormous importance for the nutritional value for human health and affect the wheat flour color. The antioxidant activity of carotenoids, together with protein, increases the nutritional and technological characteristics of flour ,87. An eAnother important trait related to wheat quality is the starch content (75% of the weight of the mature grain), which consists of two types: amylose and amylopectin. In fact, the water absorption of the dough is connected to the starch content ,89. HighFusarium graminearum, and various viruses.Major breeding programs are also focused on improving biotic stress resistance because of the severe damage to production worldwide. These kinds of stresses are generated by living organisms  causing diseases such as rust, powdery mildew, Puccinia triticina), stem rust (P. graminis tritici), and stripe rust (P. striiformis), causing losses of 15–20% worldwide [Amongst the pathogens damaging durum wheat crops at the leaf and stem levels, rust pathogens are the most prevalent. This includes leaf rust (orldwide . To dateorldwide . Durum wBlumeria graminis f. sp. tritici) is recognized as a disease of wheat of high economic importance, especially in warm climate areas where the productivity is high. Therefore, one of the most important purposes of breeding programs is to make resistant durum wheat. To achieve this goal, several studies, including a careful analysis of the environmental conditions that can influence the host–pathogen interactions, a study of the genetic and molecular interactions between the host and pathogen, and the search for new sources of resistance to be transferred in varieties, are required [Powdery mildew  in wheat represents a serious hygienic–sanitary problem [Fusarium head blight (FHB) is one of the most common and harmful diseases of durum wheat in the world. The incidence and severity of FHB depends on the weather, the areas, and the varieties used. This pathogen has caused consequential production losses together with damage to the quality of grain and the presence of mycotoxins. The presence of  problem ,94.In the Mediterranean Basin, durum wheat is cultivated under variable environmental conditions. In rainfed agricultural environments, drought stress critically constrains the crop yield. It is particularly challenging for breeders under the current unpredictable climate change to stretch the adaptability and performance stability of their cultivars. In the Mediterranean, the environment is responsible for as much as 98% of bread and durum wheat yield variations .Wheat yield is dependent on the grain number per unit area and grain weight. With climate change, yield reduction is led by a significant decrease in one or both yield components. The grain number, in turn, may be split into spikes per unit area and grains per spike. The yield components are sequentially determined and are counter-dependent. Reductions in the grain number per unit land area due to an increase in temperature have been widely reported, as has a reduction in the grain weight, which depends on the environmental conditions before flowering and during grain filling ,97. AlthBreeding for adaptation to drought is extremely challenging due to the complexity of the target environments as well as the stress-adaptive mechanisms adopted by plants to withstand and mitigate the negative effects of water deficit . The incUnderstanding the physiological mechanisms associated with drought resistance and the genetics underlying them may provide new strategies for engineering varieties resilient to drought, which is essential for wheat breeding ,107. TheAnother important constraint in crop production is heat stress. This is tightly associated with drought in the Mediterranean region as it is of particular importance when it occurs between heading and maturity, affecting the flowering and grain filling, thus reducing the grain yield . Heat stSoil salinity is considered an important limiting factor for crop production in arid and semi-arid regions. Although related to drought stress, salinity affects plant growth during the whole cycle of plant development . The lasClassically, QTL mapping has been performed predominantly in populations derived from two parents  with different phenotypic performances. The success in detecting QTL depends on the marker density, population size, and the heritability of the trait. With durum wheat, a large number of QTL studies have been performed to investigate yield performance, biotic and abiotic stresses, phenology, and quality.The grain yield and phenology are traits much studied in wheat, as reviewed in Soriano et al. ; thus, tAssociation mapping or genome-wide association study (GWAS) is a complementary approach to dissect the genetic basis of complex traits, providing broader allelic coverage and offering higher mapping resolution. This is based on linkage disequilibrium (LD), defined as the non-random association of alleles at different loci, and is used to detect the relationship between phenotypic variation and genetic polymorphisms . It is iA summary of the total number of QTL and MTAs reported in durum wheat with the analyzed traits is shown in As reported in To select the most appropriate meta-QTL for breeding purposes or candidate gene isolation, Löffler et al.  establisQTL meta-analysis has been performed in both bread wheat and durum wheat for different traits such as grain yield , crop phThe use of consensus maps integrating the most common molecular markers and the identification of consensus QTL regions among different mapping populations from different parental sources will help breeders to select the most appropriate plant material for the development of new cultivars. Redefining (and shortening) QTL regions by QTL meta-analysis will help with the identification of candidate genes for accelerating the breeding process. The use of the recent release genome sequence of durum wheat  will allscs) locus [The analysis of transcriptome through SNP arrays allowed the combination of bulked segregant analysis (BSA) and RNA sequencing to fine-map small proportions of a wheat genome involved in the traits of interest . This sts) locus . The lims) locus .As described above, the main limits of biparental QTL analysis rely on the low number of recombinant events, the limited number of polymorphic markers, and the reduced availability of genetic diversity. To overcome these drawbacks, in recent years, new breeding programs and new plant populations have been proposed, increasing the statistical power and the association between markers and traits and allowing the identification of key genes involved in the phenotypic variation . These iIn particular, the MAGIC populations are generated through inter-crossing several parental lines (eight or more) through two-way, four-way, and eight-way crosses and subsequent self-fertilization to generate recombinant inbred lines (RILs). Compared to traditional biparental populations, the use of more founders in the initial crosses allowed an increase in the recombination, consequently improving the mapping resolution and allowing greater allelic diversity. The MAGIC design was successfully realized in several crops for fine genetic mapping; however, a slow uptake has been observed in durum wheat, where a four-parent durum wheat population was produced . A succeThe NAM populations were developed through crossing several founders with the same reference line to generate a series of “interconnected” segregating inbred families. The advantage of this population is represented by the possibility of incorporating a high number of alleles in one species gene pool, accurate QTL effects and positions, and overcoming the limits of linkage analysis and association mapping approaches ,175.The NAM design for QTL mapping was developed in maize ,177 and Germplasm resources as landrace collections could be of interest for the development of new mapping populations following the approaches of MAGIC and NAM to incorporate allelic diversity. As reported by Soriano et al. , MediterOne of the main objectives of molecular genetics is to identify and isolate genes controlling important traits. Three main approaches lead to the cloning of genes of interest: positional cloning , insertiThe CG approach is based on three chronological steps: (1) choice of CGs, established through molecular and functional studies or based on linkage data; (2) molecular polymorphism in the CG to localize it on a genetic map or to calculate statistical correlations between CG polymorphisms and phenotypic variation in a set of unrelated individuals; and (3) complementary experiments  to confirm the real association of the CG in the trait variation . The CG The CG approach has been used with success in durum wheat for many traits such as disease resistance ,192, carhttp://www.wheat-expression.com/; accessed on 25 January 2021), new advancements in CG identifications can be obtained through the application of new computational methods developed to prioritize CGs in QTL before functional studies. The identification of the gene TdHMA3-B1 responsible for variation in cadmium accumulation in the grain is a successful example of the use of the genome sequence data together with the genetic variation reported in germplasm collections as the global durum panel, as reported in [Thanks to the recent release of durum wheat genome  and geneorted in .Sorghum bicolor and Setaria viridis models.Moyers  developeGenome editing is a new biotechnology allowing specific manipulation of a target genomic sequence, which has the potential to improve wheat performance and supersede traditional methods in plant breeding.TaMLO in hexaploid wheat for a gene that confers heritable resistance to powdery mildew.This technology in wheat is made more problematic due to the complexity of the wheat genome and the difficulty in genetic transformation. Despite this, recent progress in plant genome editing has been reported, above all, for bread wheat. One of the first papers using the CRISPR/Cas9 system in wheat was reported in 2014 by , who repTcLr19PR1, involved in resistance against leaf rust as reported by [Yr10, which encodes an evolutionarily conserved and unique CC-NBS-LRR sequence in wheat [Bipolaris sorokiniana with an R2R3 MYB transcription factor in wheat, TaPIMP1 [The scientific community have concentrated their efforts on resistance to pathogens such as powdery mildew ; leaf ruorted by ; stripe in wheat ; sharp ein wheat ; and res TaPIMP1 .TaGASR7, TaGW2, and TaLOX2 in hexaploid wheat and only TdGASR7 in tetraploid durum wheat were edited because they were found to be associated with grain yield components [In addition, yield was the second most studied trait; the genes mponents ,204,205.mponents ,207 repoThe recent advance in high-quality reference genomes for durum wheat along with new efficient genetic transformation strategies will accelerate the application of genome editing technology.The selection methods used in wheat breeding programs have deeply evolved with time. Classical breeding based on the phenotypic selection of a trait was the main approach used by breeders to increase crop productivity during the 20th century . This teThe development of molecular biology allowed the use of markers based on the sequence or polymorphisms in DNA for the identification of traits enhancing agronomic performance in the earlier stages of development. Marker-assisted selection (MAS) resulted in an important advantage integrated with traditional breeding methods to enhance the efficiency of cultivar development. However, MAS depends on the genetic linkage of traits with markers, and typically, only genetic loci with major effects are exploitable in this way . Since mThe availability of high-density, low-cost marker genotyping platforms has enabled a change in plant breeding by making genomic prediction and selection feasible. Genomic selection (GS) refers to the selection of genotypes using genomic information on a genome-wide scale to make selections . GenomicDifferent studies in durum wheat applied this technology for predicting the grain yield, quality traits, and disease resistance. Fiedler et al.  found prThe accuracy found by these authors ranged from 0.5 to 0.8 for single traits, increasing in multi-traits for grain yield. Zaim et al.  used fouThe development of speed breeding protocols can be considered as a useful approach to help in the development of new mapping populations and to advance the first generations in the breeding programs, which, being assisted by molecular markers, will save time for breeders, reducing the length of the breeding cycles and selecting the best genotypes. The technique implies the use of extended photoperiods in a controlled environment. For durum wheat, up to six generations in a year have been achieved . These pTo meet the food needs for the future, farmers must increase crop yields considering the climate and environment changes. Information gained from sequenced genomes in related species and in durum wheat, together with studies of fine mapping and QTL cloning, allows the identification of a high number of molecular markers, key genes, quantitative trait loci, and networks, which will lead to higher yielding crops.The decreasing cost of NGS technologies and the huge availability of sequence data on web databases allow for drastic reductions in the time required to identify candidate genes. Thus, projects that previously required more than 10 years for fine mapping, QTL cloning, and candidate gene identification could now be completed in 1–2 years for more simple traits.


C. elegans, where a single MACIT, COL-99, has been documented to function in motor innervation. Furthermore, in C. elegans, a collagen-like transmembrane protein, UNC-122, is implicated in the structural and functional integrity of the NMJ. This review article summarizes recent advances in understanding the roles of transmembrane collagens and underlying molecular mechanisms in multiple aspects of neuromuscular development and disorders.Neuromuscular development is a multistep process and involves interactions among various extracellular and transmembrane molecules that facilitate the precise targeting of motor axons to synaptogenic regions of the target muscle. Collagenous proteins with transmembrane domains have recently emerged as molecules that play essential roles in multiple aspects of neuromuscular formation. Membrane-associated collagens with interrupted triple helices (MACITs) are classified as an unconventional subtype of the collagen superfamily and have been implicated in cell adhesion in a variety of tissues, including the neuromuscular system. Collagen XXV, the latest member of the MACITs, plays an essential role in motor axon growth within the developing muscle. In humans, loss-of-function mutations of collagen XXV result in developmental ocular motor disorders. In contrast, collagen XIII contributes to the formation and maintenance of neuromuscular junctions (NMJs), and disruption of its function leads to the congenital myasthenic syndrome. Transmembrane collagens are conserved not only in mammals but also in organisms such as  For the acquisition of proper motor function, spinal motor neurons and their target muscles need to be precisely interconnected by specialized synapses called neuromuscular junctions  revealed a role for collagen XIII in the formation of NMJs  and dorsal nerve cord  phenotype. Although the functional domains and interacting molecules of UNC-122 remain unclear, it has been postulated that postsynaptically localized UNC-122 is involved in creating a microenvironment required for the structural and functional integrity of the NMJs in C. elegans.Colmedins are a family of type II membrane proteins harboring collagenous and olfactomedin domains in the extracellular region  and a compound heterozygous mutation . These include nonsense, frameshift, splice-site, and missense mutations, which are scattered throughout the gene  are a group of inherited neuromuscular disorders caused by abnormal synaptic transmission. Although over 30 causative genes have been reported, which encode proteins that function in postsynaptic, presynaptic, or basal lamina compartments, there are still CMS cases left with unidentified genetic mutations . MG is a disease caused by autoantibodies targeting components of the motor endplate, resulting in muscle weakness and fatigability. Although the majority of patients possess antibodies against AChR, an increasing number of other antigenic targets have been identified (Gilhus et al., Findings from experimental and pathophysiological studies highlight the cell adhesion properties of transmembrane collagens in neuromuscular development. These collagens have markedly different structural features from traditional adhesion molecules; in the case of collagen XIII, the approximately 150 nm long ectodomain is rod-shaped with hinges that correspond to the NC domains (Tu et al., The functions indicated by animal studies mostly explain the disease mechanisms affected by mutations in collagens XIII and XXV. However, phenotypic and histopathological changes were more severe in the gene-deficient mice than in humans. Particularly for collagen XXV, the reason that the mutational effects are limited to impairments in the EOMs in humans remains unsolved. Possible explanations might be that partial loss of function due to mutations only affects vulnerable muscle types, or that neuron-muscle interactions in humans involve a certain contribution of other molecules, including the closely related transmembrane collagens. Elucidating the molecular basis of the bioactive and pathological mechanisms of transmembrane collagens in the future will be of fundamental importance in neurodevelopmental research. Given the regulatory function of collagen XIII in the structures of NMJs and the axon-inducing effects of collagen XXV, transmembrane collagens may also have the ability to facilitate the regenerative process from nerve and muscle damage and degeneration caused by neuromuscular disorders; collagen XIII has indeed been implicated in neuromuscular synapse regeneration (Zainul et al., TW wrote the manuscript and created the figures.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.