The pelvic area is a central hypogastric area which is conformed with the pelvic inlet to reduce overlapping of the non-pelvic area and is more sensitive and specific in diagnosing female abdominal emergencies than the traditional four quadrants and nine regions methods for abdominal palpation. The purpose of this narrative review is to assess the principle and mechanism of formation of the pelvic area for abdominal palpation for women with abdominal pain. By classifying the abdominal area as inside or outside the pelvic area, abdominal pain can be located inside or outside the pelvic cavity, thereby distinguishing pelvic diseases from non-pelvic diseases. When the examiner divides the patient's pelvic area along the pelvic ring using his/her hand on the patient's contralateral abdomen, there can be a reduced pain zone under the hand and alleviate pain in the non-diseased area. It allows patients with poor perception of tenderness or abdominal pain with guarding to easily recognize pelvic or non-pelvic pain. Partitioning the pelvic area which conforms with pelvic cavity inlet can reduce confusing pelvic and non-pelvic diseases when using traditional four quadrants or nine regions method. The division of the pelvic area on the patient's contralateral side can induce a reduced pain zone under the hand and alleviate pain in the non-diseased area, which can help the patient distinguish between pelvic and non-pelvic pain. Pain is a subjective feeling to the patient, and correct patient perception of pain are the basis for a correct diagnosis. •Pelvic area conforming with pelvic inlet for abdominal palpation reduces confusing pelvic and non-pelvic diseases.•Ou MC decrescendo phenomenon (OuDP) secluding the pelvis makes pelvic area wieldy for abdominal pain location.•OuDP by the examiner’s hand on the patient’s contralateral abdomen reduces pain under the hand and the non-diseased area.•Pelvic area shows high sensitivity and specificity for locating pelvic and non-pelvic pain, thereby diseases. Even In women, the organs located in the pelvic cavity include the uterus, ovaries, fallopian tubes, bladder, sigmoid colon, and rectum. Inflammation of the female reproductive system often causes pain in the pelvic cavity. The resulting pain often overlaps with pain in other parts of the abdomen, making the diagnosis difficult ,2. There33.1The abdominal four-quadrant method does not delimit the pelvic cavity area of the abdomen and the pelvic cavity is divided into left and right sides with the vertical median plane through the umbilicus. The left and right sides of the pelvic cavity merge with other abdominal areas on both sides, which makes it easy to confuse pelvic diseases with other abdominal diseases. For example, in the right lower abdominal area, pain may be caused by diseases of the appendix, caecum, colon, kidney, or pelvic organs . Fig. 1 3.2The pelvic area is conformed with the pelvic inlet in the shape of a trapezoid with the shorter parallel formed by the upper part of the sacrum. Both sides are along the external iliac artery and the longer parallel is formed by the pubic bone . If therOu MC  manipulation is a method developed by Ou MC et al. to divide the abdominal surface into the pelvic and non-pelvic areas according to the position of pelvic cavity around the pelvic ring to easily distinguish between pelvic and non-pelvic pain, thereby distinguish pelvic diseases from non-pelvic diseases ,5.4Abdominal palpation with Ou MC manipulation (APOM) was performed by placing a hand in a chopping gesture contralateral to the patient's side along a line from the subumbilicus to the femoral arterial canal of the inguinal area while the patients were in the lithotomy or supine position with abdominal muscle relaxed , Fig. 3 In patients with abdominal muscle guarding or poor perception of tenderness, the separating hand is placed for 5 seconds or more or repeats the procedure and does not need to be pressed heavily. It will produce a pain-reduced area under the hand and alleviates the pain in the non-diseased area , which aWhen the pain in the pelvic cavity is too weak or too deep for the patient to confirm, diseases outside the pelvic cavity can be excluded by APOM first, followed by a vaginal examination to confirm weak or deep pelvic pain. Additionally, vaginal examination assisted with Ou MC manipulation can make it easier for the patient to recognize whether it is pelvic or non-pelvic pain. With OuDP induced by Ou MC manipulation, pelvic pain in vaginal examination remains with pelvic diseases, but vanishes or alleviates with non-pelvic diseases as does in APOM ,5.Fig. 451.If there is local tenderness in the pelvic area and there is no obvious tenderness in the non-pelvic area; the pain is caused by disease of the pelvic cavity.2.If there is local tenderness in the non-pelvic area, but there is no obvious tenderness in the pelvic cavity area; the pain is caused by non-pelvic disease.3.In a few cases, there is obvious tenderness in both areas, which means that there is disease on both sides or the inflammation caused by the disease of one side spreading to the other side.4.Tenderness under the hand while separating the pelvic area is not suitable for diagnosis using this method, because it may be the result of compression of large blood vessels.6A pilot prospective crossover study was initiated by Ou MC et al., in 2006 for evaluation of a cohort of 68 women with acute abdomen and a final diagnosis of pelvic inflammatory diseases (PID) with abdominal palpation with APOM and bimanual vaginal examination . All theIn another prospective study with crossover design carried out from 2006 to 2009 by Ou MC et al. , a totalThe other single blinded self-controlled study carried out from 2010 to 2011 by Ou MC et al. ,8,9 for 77.1The pelvic area is separated along the pelvic ring, involving fewer non-pelvic cavity areas than the hypogastric region. Therefore, compared to the traditional hypogastric region, the pelvic area is more precise for palpation to distinguish between pelvic pain and non-pelvic pain for its trapezoidal shape conforming with the pelvic inlet  3,5]. F,5. F3,5]In the study carried out from 2006 to 2009 by Ou MC et al. , pelvic 7.2Studies by Ou MC et al. from 2012 to 2021 on assessing OuDP have shown that it may directly normalize the dysfunctional tissue and result in an anti-inflammatory effect because OuDP renders it too fast to be considered an indirect effect . PatientThe effect of OuDP  may be r8It is understandable that if the patient's abdominal cavity has both pelvic and non-pelvic organ diseases or if complications create tenderness in another area, APOM may not be able to detect the location of the diseased organ. In addition, a woman's pelvic area mainly constitutes reproductive organs, bladder, and large intestine. If pelvic organs are removed by surgery, other diseased organs may fill the pelvis and cause significant tenderness in the pelvic cavity. APOM is an abdominal palpation technique. If the patient is incapable of communicating with the examiner regarding the perception of pain, it cannot accurately detect the location of the pain.9The proximity of intra-abdominal organs can cause significant overlap of abdominal pain presentation and the visceral pain is difficult to locate for a low density of visceral sensory innervation and extensive divergence of visceral input within the CNS, which makes the delimitation of the abdominal surface area for pelvic cavity is unwieldy and infeasible. Nonethel10Partitioning the pelvic area which conforms with pelvic cavity inlet can reduce confusing pelvic and non-pelvic diseases when using traditional four quadrants or nine regions method. By classifying the abdominal area as inside or outside the pelvic area, abdominal pain can be located inside or outside the pelvic cavity, thereby distinguishing pelvic diseases from non-pelvic diseases. The division of the pelvic area on the patient's contralateral side with Ou MC manipulation can induce a reduced pain zone under the hand and alleviate pain in the non-diseased area, which can help the patient distinguish between pelvic and non-pelvic pain. Pain is a subjective feeling to the patient. Good patient communication and correct patient perception of pain are still the basis of a correct diagnosis.The authors declare no ethical conflict to disclose.This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.All authors have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, been involved in drafting the manuscript or revising it critically for important intellectual content, given final approval of the version to be published.All studies in the review are in compliance with the register rule of Taiwan which is based on ISRCTN.All studies in the review had consents of the patients obtained.All authors are in full responsibility for the work, the conduct of the study and had access to the data, and controlled the decision to publish.Not commissioned, externally peer reviewed.The authors declare no conflicts of interests to disclose.


Gene expression via transcription-translation is the most fundamental reaction to sustain biological systems, and complex reactions occur in a small compartment of living cells. There is increasing evidence that physical effects, such as molecular crowding or excluded volume effects of transcriptional-translational machinery, affect the yield of reaction products. On the other hand, transcriptional feedback that controls gene expression during mRNA synthesis is also a vital mechanism that regulates protein synthesis in cells. However, the excluded volume effect of spatial constraints on feedback regulation is not well understood. Here, we study the confinement effect on transcriptional autoregulatory feedbacks of gene expression reactions using a theoretical model. The excluded volume effects between molecules and the membrane interface suppress the gene expression in a small cell-sized compartment. We find that negative feedback regulation at the transcription step mitigates this size-induced gene repression and alters the scaling relation of gene expression level on compartment volume, approaching the regular scaling relation without the steric effect. This recovery of regular size-scaling of gene expression does not appear in positive feedback regulation, suggesting that negative autoregulatory feedback is crucial for maintaining reaction products constant regardless of compartment size in heterogeneous cell populations. DNA, which stores genetic information, is encapsulated in a tiny cellular compartment with a lipid membrane. Catalytic proteins are synthesized by the transcription of the genetic information stored in the DNA sequence into messenger RNA (mRNA) and the translation of the mRNA sequence into a single chain of amino acids. In bacteria, these complex transcription-translation (TXTL) reactions proceed autonomously under cell-sized confinement of a few microns7. In contrast, protein complexes that regulate TXTL reactions, such as the ribosomes, have a finite size of a few tens of nanometers8. In particular, microorganisms such as bacteria densely enclose proteins, mRNA, and DNA within the cytoplasm in tiny cell bodies. In such micron-sized capsules, the ratio of the surface layer to the total volume is large, making the effect of finite molecular size not negligible. The impact of such excluded volume effects has been reported in recent studies12. With regard to in vitro systems, the addition of crowding agents such as an inert polymer in cell-free extracts induces the crowding of large protein complexes involved in gene expression, and this results in an enhancement of gene expression. In other words, the finite size of molecules is closely related to the control of intracellular reactions. Moreover, physical effects occur not only between molecules but also between molecules and boundaries14. Hence, the question of how TXTL reactions confined to a small compartment are affected is vital for understanding the physical nature of confined gene expression16 and for implementing cell-free biochemical reactors enclosed in a space as small as bacteria21.A micron-sized compartment that separates the cytoplasmic space from the exterior environment is a fundamental feature of living cells13. In contrast, the amount of protein expression in large artificial cells increased proportionally with the volume of the artificial cells. Such anomalous size dependence in confined TXTL reactions suggests that the excluded volume effect significantly suppresses protein production under spatial constraints. It should be noted that confinement-induced repression must be resolved to construct a biochemical factory utilizing cell-free TXTL reactions20. Many techniques have been developed to create artificial cells of uniform size using droplet generators and to encapsulate artificial cell reactors in devices. However, as these technologies advance and become smaller and more precise, it will be necessary to construct reaction systems that consider steric effects as well. A remaining challenge is to explore the design of scalable cell-free reactors that reduce suppression due to finite-size effects of molecules and achieve stable gene expression from the submicron-sized reactor to the test tube. It remains to be seen what mechanism is needed in confined TXTL reactions to sustain ordinary size-dependence.Previous studies have examined gene expression in cell-sized water-in-oil emulsions as artificial cells to understand the excluded volume effect in confined TXTL reactions. This approach has shown that TXTL reactions can be suppressed in small artificial cells and that the amount of protein expression is not proportional to the volume of the artificial cells22. For instance, the autoregulatory feedback where the transcription factor regulates its encoding gene has been identified widely as a “network motif” in gene regulatory networks24. In particular, negative autoregulatory feedback (NAF) control is an abundant network motif. NAF control has broad functions, fast kinetic response25, suppressing concentration variability27, mutational robustness28 and the protein synthesis on demand29. In addition, positive autoregulatory feedback (PAF) control to increase gene expression is required for functional control different from NAF, such as making multi-stable genetic switches31 and inducing a delay in gene expression kinetics32. Although there are extensive studies on autoregulatory feedback controls in gene regulatory networks, its regulatory role in confined TXTL reactions is not well understood.The key to addressing this issue is the regulatory network in the TXTL reactions, in which the amount of expressed proteins is controlled by transcriptional factors24, our findings may provide insights into the functional role of NAF control in the homeostasis of the TXTL reaction under the variability of cell size.In the present study, we investigate confined TXTL reactions with NAF in a cell-sized compartment by considering a mathematical model. We analyze the size dependence of the amount of protein expressed with the NAF control. The mathematical model shows that the anomalous size-dependent scaling is dampened in the presence of NAF control at the transcriptional level. Such size scaling approaches are close to ordinary volume dependence because mRNA synthesis is suppressed by the excluded volume effect in the small compartment and by the action of NAF control in the large compartment. In contrast, the anomalous size-scaling of gene expression does not change by PAF control, suggesting that the recovery of regular size-scaling of gene expression is unique to NAF control. It has been known that NAF control frequently appears in the transcriptional network as a network motifR, in which the molecular system for the TXTL reaction is enclosed , increases with time according to the following equation:At the translation level, the mRNAs in each layer then serve as templates for the ribosomal translation process at a different protein production rate, N on the constraint size R is worth noting. The thickness of the surface layer V and the number of protein molecules N follow the same size scaling, The focus of the present study was to reveal the size-dependence of the TXTL reactions at the steady state under confinement, and we then analyzed the fractions of mRNA in each region at the steady state , but weV and the number of protein molecules N follow the same size scaling As considered in Eq.  in the pd on Eq. . The num32. Gene networks with PAF control confers multistability in cell-fate decision. Using the similar model of confined gene expression as in Eqs.  downstream of the P32. This theoretical prediction can be examined by measuring the amount of CI protein with a GFP probe in W/O droplets of various sizes. Furthermore, similar genetic circuit would change to have positive autoregulatory feedback if the cI gene is placed downstream of the P32. Therefore, the scaling relationship of compartment volume and the amount of protein product would be able to indicate whether negative autoregulatory feedback is the mechanism that mitigates the suppression of gene expression in a confined space.Finally, experimental verification of our theoretical predictions is an important challenge for the future. Placing the


Internet of Things and wireless sensor network applications are becoming more and more popular these days, supported by new communication technologies and protocols tailored to their specific requirements. This paper focuses on improving the performance of a Wireless Sensor Network operated by the MQTT-SN protocol, one of the most popular publish/subscribe protocols for IoT applications. In particular, we propose a dynamic Quality of Service (QoS) controller for the MQTT-SN protocol, capable of evaluating the status of the underlying network in terms of end-to-end delay and packet error rate, reacting consequently by assigning the best QoS value to a node. We design and implement the QoS controller in a simulated environment based on the ns-3 network emulator, and we perform extensive experiments to prove its effectiveness compared to a non-controlled scenario. The reported results show that, by controlling the quality of service, it is possible to effectively manage the number of packets successfully received by each device and their average latency, to improve the quality of the communication of each end node. The Internet of Things (IoT) vision has become a reality, and the number of innovative applications based on smart and connected devices is growing considerably. Smart Cities, Industry 4.0 and connected vehicles are just a few examples of real use cases where everyday-life objects are equipped with sensors or actuators and communication capabilities and are able to either gather data from the surrounding environment and transmit it remotely or to receive commands and act accordingly. Considering the multifold areas where IoT applications can be deployed, the set of enabling technologies adopted is very heterogeneous. Especially for what concerns communication technologies, many different solutions are available : at the The latter especially has emerged in the last few years as one of the most popular application-layer protocols for IoT scenarios, due to its simplicity of use and low complexity. In MQTT, client devices connect to a central broker by means of a TCP connection and exchange data among themselves by means of publications and subscriptions over different topics. The broker is in charge of forwarding the data published to the clients interested in it, thus decoupling the process of data generation and consumption both in space and in time. This aspect, combined with the protocol simplicity at the client-side and the support for reliability and quality of service (QoS), makes MQTT well-suited for resource-constrained applications and motivates its great popularity.To avoid the use of TCP at the transport layer and further reduce the resource requirements, a lightweight version of MQTT named MQTT-SN has been recently proposed, specifically thought for wireless sensor networks . SimilarHowever, leaving the QoS control to each client has some drawbacks: first, the relationship between a specific MQTT-SN QoS level and the corresponding delivery performance  is not generally known to a user, as it strongly depends on the underlying network characteristics. A client may therefore request a QoS level that is too low (in case the network conditions are adverse) or too high (in the opposite case) for the desired delivery performance. At the same time, providing clients with sophisticated tools to select the optimal QoS level, such as, e.g., network bandwidth estimation , may be To overcome such issues, this paper proposes an adaptive QoS controller that is able to tune the optimal QoS level for each node, based on specific delivery performance constraints. The contributions of this paper are the following: first, we design the QoS controller in a centralized fashion, running on top of the MQTT-SN broker; the controller gathers network statistics periodically and is able to tune the QoS of each node in order to match user-defined performance constraints in terms of end-to-end latency and packet loss rate. Second, we implement the controller in a simulated environment based on the NS-3 framework: for the task at hand, we embed into the simulator the MQTT-SN protocol and run it on top of a 802.11b IoT network composed of several clients as well as interfering nodes. Finally, we perform extensive experiments and evaluate the effectiveness of the QoS controller compared to a traditional scenario where the QoS level choice is left to the user.The remainder of this paper is structured as it follows: The topic of QoS control, management and provisioning in Internet of Things systems has been a subject of recent research attention, motivated by the massive numbers of applications that communicate with smart objects using a diverse set of communication technologies and protocols. We focus here on works explicitly targeting QoS at the application layer: more complete surveys of QoS support solutions for IoT at the different layers of the protocol stack can be found in ,6,7.Application layer protocols for the IoT can be roughly categorized in two classes: RESTful and Pub/Sub approaches. Protocols such as HTTP and CoAP fall within the former class, with MQTT and MQTT-SN in the latter. Recently, some effort has been put in place to make CoAP capable of Pub/Sub interactions , somehowHTTP is the most popular application-layer protocol used in the Internet and it is clearly an attractive solution for IoT applications. However, due to its synchronous client/server pattern and high header overhead, it is often left aside in favor of more lightweight options. HTTP Quality of Services relies on the underlying transport layer provided by TCP, and it is therefore not explicitly provided.Differently, CoAP was created with the purpose of adapting the well-known HTTP functionalities to resource-constrained devices typical of IoT scenarios. CoAP is based on UDP at the transport layer: on the one hand, this choice makes it a very lightweight protocol, while on the other hand it forces to manage QoS directly at the application layer. For this reason CoAP provides a two-level QoS support for messages, which can be of unconfirmable (not acknowledged) or confirmable (acknowledged) type. In case a confirmable message is lost, it is retransmitted after a retransmission timeout (RTO), whose value is insensitive of network conditions. Indeed, the legacy version of COAP does not adapt the RTO on the basis of the network Round Trip Time (RTT). Therefore, if the RTO chosen by CoAP congestion control is below the actual RTT, CoAP will incur spurious retransmissions, which contribute to increasing network congestion. To address such shortcomings, several works proposed modification to the CoAP congestion control protocol for what concerns RTO managing ,12.The authors of  proposedIn the publish/subscribe communication pattern, all communications between nodes are made available via a broker. The broker accepts messages published by devices on specific topics and forwards them to clients subscribed to those topics, ultimately controlling all aspects of communication between devices. This allows decoupling message generation and consumption in both space and time, as well as greatly decreasing the complexity of the client side communication. For these reasons, publish/subscribe protocols are of great use in the IoT ecosystem.MQTT is the most popular pub/sub protocol currently. Based on the TCP protocol at the transport layer, MQTT allows a simple communication between devices through a central entity, named the broker, which interconnects clients sending and receiving data, named publishers and subscribers, respectively. MQTT follows the pattern of topic-based publish–subscribe communication models: subscribers can express their interest in a particular topic by issuing an appropriate request to the broker, so to receive updates every time a new message published to that topic is transmitted from a publisher. MQTT requires clients to connect to a broker before publishing messages or subscribing to topics. Each publication and subscription is coupled with a specific QoS level, chosen among three values: QoS level 0 implies fire-and-forget delivery, relying on the underlying robustness of the TCP layer; QoS level 1 refers to an “at least once” delivery, with acknowledgments required by the publisher (or to the subscriber) and retransmissions taking place in case the acknowledgment is not received; QoS 2 avoids publication duplicates with a four-way handshake among the publisher and the broker. Apart from this native QoS mechanism, several works have addressed other options for improving the quality of MQTT transmission; in , an adapFinally, very few works have addressed QoS control for the MQTT-SN protocol, the lightweight version of MQTT developed for resource-constrained scenarios. The work in  analyzesThe closest work to ours is , where aThe MQTT protocol has emerged as the gold standard for IoT applications: as a matter of fact, all major cloud platforms  expose their IoT services through MQTT, and many IoT-related enterprises provide MQTT-based data collection and communication solutions.At the same time, MQTT requires an underlying reliable transport layer, such as TCP, that provides an ordered lossless connection capability. This may be either unavailable or too complex to handle for very simple, small footprint, and low-cost devices such as wireless sensors or in general smart devices used typically in IoT scenarios. This reason motivated the release of an adaptation of the MQTT over the UDP transport layer, with reduced size messages and additional lightweight functionalities to fit perfectly to the peculiarities of wireless sensor networks. This protocol takes the name of MQTT-SN (MQTT for Sensor Networks): a simple sketch of its architecture is reported in Giving the clients the capability of controlling the QoS to use for each published message allows the adaptation to different and dynamic network conditions. However, selecting the best QoS is not a trivial choice for clients, which may not be aware of the underlying network characteristics. Under bad network conditions, increasing the QoS clearly leads to an increase of the reliability, but at the cost of increasing the number of messages produced and the overall delay in the communication . Finding the best trade-off and hence the most performing QoS to use for sending/receiving messages is crucial for improving the communication efficiency. In the particular scenario of WSNs, bringing intelligence into the constrained devices in order to automatically adapt the QoS is not always possible, due to resource limitation. For this reason, this work sets the goal of building a reactive controller to be placed in the gateway/broker with the goal of optimizing the application layer quality of service of each client analyzing simple and easy to compute performance indicators derived from the communication network.https://mosquitto.org/ (accessed on 15 October 2022))), in this work we opt for a simulated approach, which allows us to better control the network characteristics as well as perform large scale experiments easily. For the task at hand, we implemented a version of the MQTT-SN protocol in the NS-3 network simulation environment (https://www.nsnam.org/ (accessed on 15 October 2022)). The framework allows us to simulate a network from the application down to physical layer, furthermore allowing us to compute and keep track of all measures needed in order to build the QoS controller . We focus particularly on the characteristics of the MQTT-SN protocol which are related to QoS (publish–subscribe mechanisms over UDP with retransmission mechanisms), without considering technical aspects such as, e.g., connection procedures at the application layer, topic registration or last will and testament messages.Although some implementations of MQTT-SN brokers are available . The network topology is illustrated in Publisher nodes: The origin of the MQTT-SN traffic; these nodes send messages to the broker at a certain rate with a specific payload to be delivered to all the subscribers (a single common topic is used)Subscriber nodes: The receiving end of the publish messages. After correctly receiving a packet from the publisher, the broker has to forward the publications to the subscribers that requested it Interference nodes: These nodes generate generic non-MQTT-SN traffic and have been introduced to create undesired traffic to mimic a real network scenario in which several devices share the wireless channel.In order to simulate a real WSN use case, we implemented three different types of client sharing the same 2 Mbps channel:To simplify the scenario, we consider only one topic in the network: therefore, every message transmitted by a publisher is received by the broker and forwarded to all subscribers. For each message published by the clients (or forwarded by the broker to the subscribers), the simulator allows to control its QoS level: specific retransmission procedures, where applicable, are implemented following the best practices of the MQTT and MQTT-SN protocol specification. For QoS 0, no retransmissions are expected. For QoS 1, the standard specifies a retry timer For what concerns the hitrate, unsurprisingly, when the network conditions are favorable (no interference traffic and low BER), the delivery performance is good regardless of the QoS level used. When the BER or the level of interference traffic increase, all QoS configurations except for As for the end-to-end latency, similar observations can be derived. In the case of good network conditions (low BER and interference traffic), all QoS levels show in generally low latencies. The implemented simulation framework allows us to perform several tests in order to evaluate the delivery performance obtained by MQTT-SN clients in different network conditions. In particular, the following two performance metrics can easily be computed: (i) the hitrate, defined as the percentage of the published MQTT-SN messages that successfully reach the subscribers, and (ii) the end-to-end latency from a message publication to its arrival at the subscribers. We run several tests where one single client transmits 1400-bytes publish messages at a fixed rate of 1 msg/s to 10 subscribers. Each test is characterized by different underlying network conditions, in terms of the Bit Error Rate (BER) of the wireless channel as well as the amount of traffic produced by interference nodes. Moreover, in each test we controlled the QoS requested by both publishers and subscribers to evaluate its effect on the delivery performance. Let W seconds: at the end of each temporal window, the broker estimates two performance measures for each subscriber:Current hitrate  for evaluating the percentage of correctly forwarded messages, the broker waits for explicit notification from each subscriber. In other words, every W seconds each subscriber publishes to the broker the number of received messages r. In such a way, the broker can estimate the hitrate as the ratio between r and the number of forwarded messages f.Average end-to-end latency  depending on the resources available on the clients, two options are available. If clients are all synchronized, the creation timestamp of each publication might be inserted in the publish messages so that subscribers might directly compute the end-to-end delay and notify to the broker its average value every W seconds. However, synchronization of clients might be complex to obtain in such a resource-constrained scenario. As a cheaper alternative, we propose to perform end-to-end delay estimation directly on the broker, by observing the number of application layer retransmissions performed. Indeed, the latency experienced by publish messages is directly correlated with the number of retransmissions performed by the broker towards the subscribers. The broker may therefore learn the relationship existing between latency and number of retransmissions and estimate the former from the latter. To verify this, we have used the simulation framework to transmit messages at different bit error rate values. PUBACK message at the broker) and QoS 2 messages  depending on the number of retransmission observed. As one can see, there is a direct relationship between the number of retransmissions and the end-to-end latency: this happens because the application layer retransmission timer The QoS controller has been designed to run on board the broker, leveraging its central role in the network as well as its superior computational and memory resources compared to general IoT clients. The main rationale behind the controller creation is the following: instead of asking for a particular level of QoS, IoT clients subscribing to the broker request a specific delivery performance, either in terms of hitrate or end-to-end latency. The QoS controller then selects the most appropriate QoS level to satisfy the clients’ requests. Furthermore, as emerged from the results shown in Latency-constrained mode: in many real time and mission critical IoT applications [ications , the subHitrate-constrained mode: to address delay-tolerant scenarios that require a specific amount of hitrate performance, the controller can also be set to work so as to minimize the end-to-end latency subject to a minimum hitrate constraint. The state diagram for this operation mode is illustrated in Autonomous mode: Finally, the proposed QoS controller can also work in an autonomous mode, which seeks the best trade-off between latency and hitrate for each subscriber  the topic over which the QoS controller should work, (ii) the operational mode and (iii) the operational parameters (constraints and thresholds). Examples are illustrated in Feedback communication: Regardless of the operational mode selected by subscribers, the controller needs to be aware of (i) the current hitrate and (ii) the average end-to-end latency experienced by nodes in the current evaluation window. While for the latter metric the broker can perform an estimation based on the number of retransmissions performed, for what concerns the current hitrate each client needs to explicitly communicate the number of received messages in the previous evaluation window. Such an exchange may happen on a special control topic, such as, e.g., $HITRATE/topic. In such a way, the broker can be aware of the hitrate each client is observing on the topic topic.Controller deactivation: A client has two choices for deactivating the controller: (i) unsubscribing from the topic over which the control is active, and resubscribing without activation or (ii) transmitting an explicit message on the special topic $QoSControl/Dectivate, passing as payload a JSON file where the value of mode is equal to off.We highlight that, to provide the maximum degree of flexibility, the proposed controller works independently on each subscriber requesting its activation. Indeed, each subscriber connected to the broker may: (i) not use the controller and rely on the standard QoS levels, (ii) activate the controller specifying its own latency or hitrate constraints or (iii) use the controller in autonomous mode.We performed several simulation experiments to test the performance of the proposed QoS controller, using the same network configuration illustrated in During all tests, the simulation is performed by setting one publisher to send periodically one 1400-byte message every second, for a total duration of 5 min. For the first 120 s of the execution, the controller is left shut down to show the behavior without its adjustment. After 120 s the controller starts its execution by checking the metrics of the previous messages and adjusting the QoS of the subscribers, if needed. Finally, to avoid continuous change of the QoS of a single subscriber, the controller can not change the QoS of the same subscriber in two consecutive intervals.Autonomous mode: As a first experiment, we run the simulation framework with five subscribers, activating the controller in autonomous mode after 120 s. The following parameters have been used to setup the controller: Hitrate-constrained mode: As a second experiment, we run the same network simulation as before, activating the controller in hitrate-constrained mode. The following parameters have been used: the target hitrate Latency-constrained mode: Finally, we also report the runtime behavior for a scenario where the controller was run in latency-constrained mode, setting the target latency Finally, we performed several experiments to compare the performance of the proposed controller with the ones obtained by (i) the legacy MQTT-SN QoS levels and (ii) the method proposed in  (indicatTo perform a fair comparison with the method in , we perfAt the end of each simulation, we aggregate the results obtained using their average and standard deviation. The results are illustrated in This work has presented the design and implementation of a QoS controller for MQTT-SN applications built on lossy wireless networks. In such cases, using the fixed QoS levels provided by the MQTT/MQTT-SN standards may be not straighforward, since the actual QoS experienced by nodes depends on the underlying network characteristics, which are generally unknown to clients. Therefore, we propose a controller that runs on the broker and accepts as input from the subscribers explicit performance delivery constraints in terms of average latency or hitrate. The controller is able to work in different operational modes to optimize the preferred metric, as well as in an autonomous mode, which seeks the best tradeoff between the two. We implemented the controller in a simulation environment based on the ns-3 network simulator framework and tested its behavior in a variety of cases. We performed several experiments to validate the simulation framework, show the live behavior of the proposed controller and summarize its performance. Future research directions will target the real-life implementation and testing of the proposed controller in one of the available MQTT/MQTT-SN brokers.


The development of a new dynamic lattice element method (dynamicLEM) as well as its application in the simulation of the propagation of body waves in discontinuous and heterogeneous media is the focus of this research paper. The conventional static lattice models are efficient numerical methods to simulate crack initiation and propagation in cemented geomaterials. The advantages of the LEM and the developed dynamic solution, such as simulation of arbitrary crack initiation and propagation, illustration and simulation of existing inherent material heterogeneity as well as stress redistribution upon crack opening, opens a new engineering field and tool for material analysis. To realize the time dependency of the dynamic LEM, the equation of motion of forced vibration is solved while using the Newmark- The accurate identification of the structural state requires consideration of all disturbances within the structure, such as small damages or material heterogeneity. In general, the dynamical methods differ in structural dynamics based on the analysis of waves to determine existing damages and wave-based methods to identify the location of the discontinuities2, often with high frequency waves to detect the damages4. So far during the past decades, mainly continuum-based approaches were developed to detect the damages in the structures5. Finite element analysis is implemented to analyse and detect cracks in a cantilever beam6. Advanced wavelet-based neural networks are considered in the detection of damages in beam-like structures7. Similarly, a numerical scheme is proposed for the detection of multiple cracks in three dimensional (3D) structures8. Within the scope of heterogeneous elasticity, a dispersion equation is derived for a heterogeneous layer over an inhomogeneous half-space, in which the propagation of Love waves are influenced by inhomogeneity parameters9. Likewise, the dynamic behavior of multi-layer heterogeneous composite magneto-elastic structures for surface wave scattering is investigated, where a finite difference technique is derived to obtain the group and phase velocities10. In another study, it is found that because the medium tends to be elastic as the viscoelastic parameter decreases, the propagation of torsional waves faced considerable hindrance in spreading further11.In civil engineering and material science, continuum-based methods such as boundary element, finite element, finite difference and volume element methods are the most common practices to simulate the dynamic wave propagation through solid bodies. These methods have been developed under the consideration of a wide range of boundary conditions from the 70’s until present day. However, the simulation of structural vibrations or wave propagation in solids under the consideration of damaged or heterogeneous structures cannot easily be performed using the continuum-based methods. In particular, the consideration of local damages or a stochastically distributed heterogeneity is difficult to realize. The structural health monitoring methods are widely considered and implemented in engineering applications to ensure the usability as well as the state analysis of structures, and finally the failure of structures13. The standard Discrete Element Method (DEM) on the other hand, as an inherent Lagrange based method, is considered for the simulation of large particle movement in non-cohesive granular materials14. Application of CDEM is also expanded in different scientific fields, such as simulation of crack propagation in a vitreous biopolymer material15. Coupled FED-DEM methods are developed to tackle the computational costs that are associated with discrete methods and are able to simulate fracking while considering the irregularities that exist in geomaterials16. However, mainly due to the great numerical costs, the discrete simulation methods were barley considered until now to simulate a full dynamic or wave field problem in engineering applications. To decrease the computational costs and lower the problems of complexity that exist for analysing discrete structures, the so-called lattice element method (LEM) has been developed. The initial idea was to analyse the crack propagation in solid and heterogeneous materials, like concrete17.Besides the continuum-based methods, the development of discrete methods has become very popular to model micro- and meso-scaled problems, mostly to understand the phenomenon of material behaviour. Discrete methods, such as cohesive Discrete Element Method (CDEM), are used, e.g. to simulate the fracking in cemented geomaterials18. The lattice elements are able to simulate the frack initiation and propagation in solids and cemented geomaterials, where small particle movements or displacements are expected22. One of the main advantages of the LEM related to other numerical methods is its ability to simulate the stress redistribution and concentration upon the cracking process. The domain can be represented in the simplest way by a series of springs19, more complex than Euler Bernoulli or Timoshenko beams23. The generalized beam lattice model, in which mechanical bond-aggregate interface properties are implemented in a lattice element, is also used to simulate the crack growth in concrete24. The Rigid-body-spring networks are also similar to lattice elements, representing the domain with a series of spring elements. In these methods, between each unit cell, three spring elements representing a resistance against axial, transverse and rotation displacements are considered27. The initial step in application of the lattice model is to grant the mesh-independence outcome of the simulations. In this regard, the embedded strong discontinuity is introduced into lattice elements, resulting in mesh-independent computations of failure response23. In another approach, the correlation between single element and continuum properties is achieved using the strain energy theory, where a strain energy stored in a unit cell is equal to the strain energy stored in a continuum29. The crack simulation in lattice elements is performed by removing an element reaching its strength threshold (brittle failure) or decreasing the stiffness according to a bi-linear softening scheme. The failure of elements is defined based on the Mohr–Coulomb failure model with a tension cut off25 or based on mode I and II fracture mechanisms, where a critical stress intensity factor is used to calculate the released strain energy rate31. In all the aforementioned developments, the focus was on the study of fracture propagation in materials.The LEM can be considered as a hybrid method, where the generated domain can be either seen as a continuum or as packed discrete particles32, in unsaturated granular geocomposites33 or modified geomaterials34 have all been investigated. The hydro-mechanical dual lattice models are developed to simulate the flow and permeability changes in geomaterials36. The dual lattice model was also implemented to investigate the fluid-driven percolation and developed flow paths in salt and clay stone samples under anisotropic confining stresses18. The initial approach to extend the common lattice element method into the field of dynamics for use of wave field simulation in heterogeneous materials has already been developed37. Here, the dynamic LEM is used to simulate wave fields while considering a progressive fracking process in brittle and quasi-brittle material under dynamic loading. Additionally, the dynamic LEM is applied to solve the problem of mechanical waves in rock mass or cemented granular material under dynamic loadings38. The simulation of crack propagation under dynamical forces by an embedded strong discontinuity approach is also implemented39. The simulation results of the dynamicLEM are used to train the artificial neural networks (ANN) model to detect the location of the discontinuities40, thus reducing the computational costs.Over the last few years, the application of lattice elements has been extended to simulate the coupled thermo-hydro-mechanical processes. The heat transfer and change of effective thermal conductivities in cemented geomaterialsThis research paper extends the application of the lattice element method in dynamic structural analysis to tackle the effect of discontinuities and inherent material heterogeneities on wave fields. The inherent mesh irregularity of the lattice element and stochastic fracking process makes the model suitable for simulation of discontinuities under expected small deformations. Initially, the implemented mathematical methodology is explained, where Newton’s second law within the equation of motion is solved in the time domain to determine the accelerations, velocities and displacements of generated nodes. Afterwards, validation benchmarks are presented and the results of the dynamic lattice for a 2D domain is compared to the boundary element method (BEM) solution. Within that benchmark, the dynamic structural behaviours are compared in the frequency domain to demonstrate the accuracy of the method. To show the influence of discontinuities, a series of full wave field simulations are performed and analysed. The visualization of the wave types within propagating wave field, the wave field scattering and dispersion at heterogeneities, as well as the wave field shadows around discontinuities illustrate the excellent ability of the new dynamic method and its future applications in structural dynamics and structural health monitoring.41. In the implemented discretization method, the generated irregularity of a mesh is controlled using the defined randomness factor 32. The location, orientation and length of the generated discontinuities are in a stochastic manner as shown in Fig. The general discretization of a domain is performed using the vectorizable random lattice43. In these models, granular particles, cement  and interfaces are defined with different mechanical properties. Figure One of the advantages of considering the lattice model is its simplicity of generating a heterogeneous granular domain. Therefore, this numerical method can easily be used in crack simulations in e.g. concrete material, where granular particles are cemented with each other44. Implementing the spring elements eliminates the simulation difficulties that arose from short beam elements, such as a small beam aspect ratio. Each generated nuclei has three degrees of freedom and Fig. A is the cross-section area , first stiffness coefficient , is driven based on the continuum stress condition and with the linear elastic theory. According to this correlation, the desired Poisson’s ratio  and tensile strength , the stiffness of the element is reduced.The regularisation of a lattice model is carried out according to the strain energy theory and energy balance, where the stored energy in a continuum (t) are calculated according to the applied external load. The mass matrix  for an elastodynamic problem. The benchmark considered here is a plane P-SV-wave propagation within a plane strain domain. The analytical solution for the one-dimensional shear-wave propagation problem can be easily obtained50:t is the traction acting on the domain boundary c is the jump-term which depends on the geometry of the source point.The BEM is solved according to the boundary integral equation method (BIEM) and with use of a collocation procedure, whereas it can be written asnth order; l and k are the direction of the source load and the receiver response, respectively. Further description of the classical formulation can be found in50. After discretization, Eq.  shifted during the simulations from 1 to 50 Hz, where the angular frequencies are equal to s. The similar material properties and boundary conditions are considered. The total number of generated elements using LEM is 10561, where the minimum wavelength (l) ratio is 21. For both the dynamic lattice method and BEM, the structural dynamic responses under different circular frequencies (rad/s) are illustrated in Fig. With use of the dynamic lattice method, as described in “50. The results of the analytical solution of the resonance frequencies  obtained from the Eq.  only varies with the assigned different effective stiffness values.NC - homogeneous beam element with no damageSC1 - homogeneous beam body with predefined single crack, where the crack orientation is perpendicular to the loading directionSC2 - homogeneous beam body with predefined single crack, where the crack orientation builds an angle of 45A single rectangular pulse is excited through receiver sensor R5, EX5. The simulated results according to dynamicLEM are provided in Figs. In order to investigate the disturbance of wave fields in fractured materials, a cantilever beam body as shown in Fig. In the next series of the results, multiple predefined discontinuities are generated inside the homogeneous beam element as shown in Fig. N is excited from the NC - CH1 - CH2 - CH3 - CH4 - One of the main advantages of the lattice element method over conventional continuum methods is its ability to simulate the discontinuities while accounting for the inherent heterogeneity and irregularities in the particle scale. The irregularities such as the shape factors are already implemented while considering the irregular meshes and the defined randomness factor. In solid geomaterials, such as concrete or rock, the domain is composed of granular particles, cement  and the bond-particle interfaces as shown in Fig. The qualitative and quantitative results of DynamicLEM in a heterogeneous concrete body are presented in Fig. i and j. This also highlights the importance of micro- and nanoscale THM properties in heterogeneous material, which requires further investigations.As a follow up, qualitative and quantitative analyses of a wave field disturbance under multiple heterogeneity ratios are performed for a medium with randomly distributed heterogeneity. In this setup, a stochastic distribution of heterogeneity in each generated Voronoi cell is carried out. Therefore, instead of a cluster of cells representing a similar component as shown in the previous example, in this example, each Voronoi cell individually represents a unique mineral. The beam body without any predefined discontinuity is composed of four different minerals as shown with dark blue (DB), light blue (LB), red (R) and orange (O) cells in Fig. NC - homogeneous domain, where RH1 - heterogeneous domain with two distinct minerals, where RH2 - heterogeneous domain with two distinct minerals, where RH3 - heterogeneous domain with four distinct minerals, where RH4 - heterogeneous domain with two distinct minerals, where RH5 - heterogeneous domain with two distinct minerals, where RH6 - heterogeneous domain with two distinct minerals, where RH7 - heterogeneous domain with four distinct minerals, where RH8 - heterogeneous domain with two distinct minerals, where RH9 - heterogeneous domain with four distinct minerals, where Similar to the previous setup, a single rectangular pulse is excited through the R5 reference point (Ex5). With increasing the wave velocities, the time step is decreased to grant the convergence of the numerical solution. Therefore, the simulation time step is reduced to In order to investigate the effect of a heterogeneity ratio on wave fields and to determine the critical heterogeneity ratio threshold, the following ratios are considered and simulated:Increasing the stiffness heterogeneity between particle, bond matrix and aggregate-cement interface induces excessive disruption on the wave fronts. The magnitude of the reflected wave fronts is increased when the ratio of the stiffness is enlarged. The recorded and plotted data are dependent on the natural frequency of a beam structure. In all of the setups presented for a concrete body, the assigned masses (generated mesh) on Voronoi cells are constant. With increasing the stiffness of a domain, the wave velocity also becomes greater. Although the increment of the heterogeneity induced wave dispersion in the concrete body, the evaluation of the outcomes based on the arrival of P and SV waves is still possible.In a rock domain, the effect of heterogeneity ratios as well as number of minerals on wave disturbance is analysed. Similar to the concrete body, increasing the stiffness heterogeneity induces excessive disruption on the wave fronts. In contrast to the concrete body, after a certain stiffness heterogeneity ratio, the evaluation of the outcomes based on the arrival of P and SV waves is not possible. This is clearly visible from the plotted time histories, starting from RH5, where at around The total number of minerals in a rock domain has an effect on the disturbance of the wave fronts. This is clearly distinguishable when comparing the RH8 and RH9 results, where the maximum stiffness heterogeneity ratio is equal to 200. In RH8, the disturbance of wave fronts starts from While comparing the rock and concrete bodies it is clear that in a heterogeneous rock body of a similar stiffness heterogeneity ratio, the wave dispersion and scattering is higher. In contrast to CH4, in RH9, where in both setups the maximum stiffness heterogeneity ratio is equal to 200, the evaluation of the arrival of P and SV waves is not possible. This again emphasises the importance of considering the inherent heterogeneity in numerical simulations. Identification of discontinuities based on arrival of first and second P and S waves will then be a challenging task, which requires further investigations.A small stiffness transition zone  produces a larger variation of P and S wave velocities, which leads to larger reflections and dispersion of waves. Increasing the thickness of the transition zone  results in gradual variation of seismic velocities. This also reduces the energy of reflected and diffracted waves, which eventually has less impact on the disturbance of wave fronts.Besides the simulation of wave propagation under isotropic conditions, the dynamic LEM is able to simulate wave vibrations in an anisotropic media. Based on the presented results for both concrete and rock type geomaterials, it can be concluded that:In this study, we investigated the application of a 2D dynamic lattice in the simulation of displacement wave fields in a fracked and heterogeneous medium. The proposed dynamic lattice model based on the equation of motion of forced vibration is not only used to simulate the displacement wave fields in homogeneous domain but also to illustrate qualitatively and quantitatively the disturbance of wave fields in the discontinuous and heterogeneous medium. It is also possible to detect the defects using the dynamic lattice model, in which a high-frequency wave can be generated and recorded in the reference sensors. The dynamic lattice is also capable of detecting the location of the initiated crack in a stressed structure. In order to validate the in-house developed 2D dynamic lattice, the displacement vs. angular frequency results of a 2D plain strain domain are compared to the boundary element method (BEM) solution. It is shown that with larger wavelength to element length ratios, the lattice results provide great accuracy.The advantage of dynamic lattice in the displacement wave field simulation lies in its embedded irregularity in domain discretization, its ability to define discontinuities and particle heterogeneity, simulate frack initiation and propagation as well as the stress redistribution and concentration upon crack propagation. Similar to discrete methods, the lattice method also considers the inherent heterogeneity in particle scale. To better illustrate this advantage, the developed dynamicLEM is used to simulate the displacement wave fields in heterogeneous concrete and rock geomaterials. The results indicate that with increasing the maximum heterogeneity ratio (stiffness ratio), the disturbance of wave fields becomes greater. After a certain heterogeneity threshold, the quantitative evaluation of dynamic results with conventional methods is not possible. It is also shown that increasing the number of mineral compositions in a rock body has a considerable effect on the wave field disturbance. Comparing the rock and concrete beam bodies indicates the importance of mineral distribution scheme and particle sizes on dispersion of wavefronts. It is shown that in a same stiffness heterogeneity ratio, the reflection and diffraction of waves in a rock domain  are stronger than a concrete domain, where a cluster of Voronoi cells represent an individual aggregate. The results cohere with the theory, where a small stiffness transition zone  produces a larger variation of P and SV wave velocities compared to a larger transition zone . The simulations are performed on a Desktop-PC with a Xeon processor (2.10 GHz) with a total number of 16 cores, and the computational time for a single simulation is approximately 24 hours. In the developed explicit algorithm, parallel computing is partially implemented, however the main solver is dependent on a performance of a single core. For larger domain simulations, parallelization and optimization of the developed algorithm is essential.40. The recorded displacement wave fields at each reference point in dynamicLEM are considered as training data for developing an ANN method to not only decrease the computational costs but also increase the accuracy of the crack predictions. Eventually, for any given wave spectrum that can be obtained from the field applications, the ANN method is able to predict the location, length and orientation of the existing discontinuities.The dynamic lattice element is capable of modeling the wave field’s disturbance and dispersion when a discontinuity and heterogeneity in the domain is present. The identification of discontinuities in heterogeneous materials is a challenging task, where particle scale models similar to dynamicLEM can provide accurate outcomes. However, the simulation of large domains with high frequencies requires a substantial number of elements, not only to model the particle size effect but also to reach greater wavelength to elements length ratio and avoid numerical difficulties. One way to overcome this obstacle is to consider the artificial neural networks (ANN) models to predict the location of the discontinuities in the homogeneous and heterogeneous bodies


An 8-year-old child presented with fatigue, weight loss, and visual deterioration. MRI demonstrated a craniopharyngioma with compression of the optic chiasm and extensive edema on the hypothalamus and optic radiations. The tumor was completely removed via an endoscopic endonasal approach. Postoperatively, vision improved and hypothalamic edema completely resolved within 5 days. This video demonstrates the technical nuances of the surgery and discusses the impact of surgery on the hypothalamic nuclei in pediatric patients.https://youtu.be/wxkBmhTPi6c.The video can be found here:  This video will document the endoscopic endonasal resection of giant pediatric craniopharyngioma and also discuss the issue of hypothalamic invasion and impact of surgery on hypothalamic edema.0:33 This 8-year-old girl presented with 2-month history of fatigue, early satiety with 5 pounds weight loss, blurry vision, and falling off of her growth curve.0:42 CT and MRI revealed very large solid and cystic craniopharyngioma with hydrocephalus, and a calcified and cystic component consistent with adamantinomatous craniopharyngioma. There was extensive edema within the hypothalamus and the optic radiations, which raises the question of whether the tumor should be completely removed for cure or just debulked to avoid damaging the hypothalamus but necessitating postoperative radiation therapy.1:05 In order to relieve her hydrocephalus, intraventricular endoscopic biopsy and cyst fenestration were performed. Visual improvement was noted as well as improvement in hydrocephalus. Patient went home to trick-or-treat for Halloween and then returned 10 days later for definitive resection of the tumor.1:15 The questions to be addressed are, which approach to choose, endonasal or transcranial; how does edema in hypothalamus influence the decision; and what are the key steps of surgery and the closure? For this reason, we chose to perform an endoscopic endonasal approach for this child. Our results in adults have shown gross-total and near-total resection in approximately 95% of patients and CSF leak rate about 3%. However, in pediatric cases, resection of tumor in hypothalamus has been shown to increase in BMI. In these studies, the rate of recurrence after subtotal resection and radiation are not adequately documented as there is much shorter follow-up in subtotally resected patients and the authors used reoperation as a surrogate for recurrence, so cases lost to follow-up or who go to other institutions or pass away are not included. In addition, when examining the hypothalamic nuclei involved in satiety and body weight, they are found in the lateral walls of the ventricle, not the floor. In our prior study of endonasal endoscopic resection of pediatric craniopharyngiomas, BMI increased in only 20% of the patients despite a 50% rate of gross-total resection.In extensive review of literature comparing endonasal to transcranial surgery for craniopharyngioma, it is demonstrated that gross-total resection rates are similar, CSF leak rates now comparable as well; however, visual improvement is markedly higher following the endoscopic endonasal approach.2:50 Craniopharyngiomas can be found in sella, sellasuprasellar or only suprasellar, as well as only in the third ventricle. The key surgical steps are first to sweep the superior hypophyseal arteries laterally and superiorly to preserve the blood flow to the chiasm and dissect under the chiasm to expose the plane between the tumor and hypothalamus and preserve the lateral wall of the third ventricle, remove the superior extent of the tumor, preserve the stalk as long as possible, and then sacrifice the stalk if required to achieve a gross-total resection.3:16 First, the endonasal endoscopic approach is performed. The ostia are identified bilaterally. Bilateral nasoseptal flaps are elevated, the posterior septum is removed, a bilateral sphenoidotomy is performed. Given this is a pediatric patient, extensive drilling is required to enlarge the sphenoid sinus to create large enough corridor for surgery. This involves opening up the anterior wall, resecting superior turbinate bilaterally, and performing posterior ethmoidectomies. We try to save the middle turbinates as much as possible and only resect the turbinate on the left if we need more room for the scope.Next, we open up the sella and the tuberculum with diamond drill and Kerrison in order to make an adequate opening. Doppler is used to identify the blood vessels. An opening is made in the bond from the top of the sella to just above the chiasm in the tuberculum. The dura is opened. A large sellar craniopharyngioma is identified and this first internally decompressed, and then we start to dissect the tumor from the surrounding neurovascular structures, first preserving the superior hypophyseal arteries. Sweeping movement was used to dissect the arachnoid off the lateral region of the tumor away from the PComm. Inferiorly, the basilar artery can be identified in the inferior aspect of the tumor. We also can identify the mammillary bodies which have to be preserved. Here we are sharply dissecting the posterior aspect of the tumor off the mammillary bodies. Here we are mobilizing the tumor away from the hypothalamus and then additional internal decompression is done. The plane between the tumor and the hypothalamus/lateral walls of third ventricle is very carefully dissected under direct vision. Normal hypothalamus is swept laterally; this also done with the mammillary bodies inferiorly sharply using microscissors if the plane is unclear. Again, the lateral walls more superiorly are dissected free from the tumor under direct vision to expose the lateral walls of the third ventricle/medial hypothalamus and superior hypothalamus in both sides. The tumor extends superiorly and the superior wall is equally and carefully and bimanually dissected free from the hypothalamus in order to minimize any damage to the hypothalamus, which is the goal of surgery in this pediatric patient. The tumor is then removed underneath the chiasm. Inspection shows that the lateral walls are intact with some petechial hemorrhage.6:07 The closure was performed with AlloDerm and gasket seal using rigid buttress to keep it in place and vascularized nasoseptal flaps for final coverage, held in place with tissue seal.6:21 Postoperative MRI scan reveals gross-total resection of the tumor, but most interestingly, in this case, that the edema in the hypothalamus and optic tracts in the MRI scan 5 days postoperatively was gone, raising the question of whether surgery to remove the tumor completely is not a better strategy to preserve hypothalamic function than subtotal resection leaving tumor invading the hypothalamus followed by radiation.6:50 The patient recovered well and her vision improved. The lumbar drain was removed in 2 days. She was cognitively intact with panhypopituitarism and went home on postoperative day 6.


Understand what progress has been made toward a functionally predictive lower urinary tract (LUT) model, identify knowledge gaps, and develop from them a path forward.We surveyed prominent mathematical models of the basic LUT components  and categorized the common modeling strategies and theoretical assumptions associated with each component. Given that LUT function emerges from the interaction of these components, we emphasized attempts to model their connections, and highlighted unmodeled aspects of LUT function.There is currently no satisfactory model of the LUT in its entirety that can predict its function in response to disease, treatment, or other perturbations. In particular, there is a lack of physiologically based mathematical descriptions of the neural control of the LUT.Based on our survey of the work to date, a potential path to a predictive LUT model is a modular effort in which models are initially built of individual tissue-level components using methods that are extensible and interoperable, allowing them to be connected and tested in a common framework. A modular approach will allow the larger goal of a comprehensive LUT model to be in sight while keeping individual efforts manageable, ensure new models can straightforwardly build on prior research, respect potential interactions between components, and incentivize efforts to model absent components. Using a modular framework and developing models based on physiological principles, to create a functionally predictive model is a challenge that the field is ready to undertake. The goal of this review is to synthesize the body of LUT modeling efforts, highlight modeling attempts to predict clinically relevant phenomena, and provide a perspective on how to move toward creating a functionally predictive model of the LUT.It is difficult to make precise predictions about lower urinary tract (LUT) function because its behavior arises from the complex interactions of many tissues and organs spanning both space  and time (from minutes of slow bladder filling to seconds of dynamic voiding). Mathematical models of physiological systems can characterize these multiscale interactions which enable us to make otherwise impossible quantitative predictions about how diseases or therapies affect their function through computational simulation. Currently, there is no functionally predictive mathematical model of the LUT, and without it, our only recourse to deepen our knowledge of its behavior and prototype new therapies is through slow in vivo experimentation. With an estimated 60% of adults developing at least one lower urinary tract symptom (LUTS) by age 40,4 For example, therapeutic strategies for neuromodulation have been found without a clear understanding of their mechanisms of action,5 leading to speculation that we could design more effective versions if we understood how the mechanisms worked at a fundamental level. A mathematical model that captures the function of the LUT could be used to systematically evaluate simulations of different neuromodulatory parameters  that would identify the most effective methods of treatment. The results of such in silico experiments could then lead to more focused in vivo experimentation to validate the results of the simulations and help clarify its mechanisms of action.A mathematical model can serve as a virtual laboratory that complements observations from experimental data and can provide insight into the mechanistic function of organ systems or potential therapeutic strategies.To coordinate efforts in the development of a model of LUT function, the state of LUT modeling must be compiled and evaluated so that gaps can be identified and targeted by future modeling efforts. Here we will discuss the common strategies for modeling the three key components of the LUT . The bladder and urethra have adequate representations that reflect the biomechanics and fluid dynamics of the LUT, but available models of the neural control are rather insufficient, which is largely due to difficulty discerning corresponding neural activation to variations in LUT biomechanics and fluid dynamics. In our review of the literature, we compiled the prominent models that we believe facilitate the conception of a fully predictive model. In the chord diagram of 6 From a technical standpoint, the greatest difficulty might not be the creation of models of individual components, but rather the connections between such models. An appropriate choice of model structure could facilitate the production of these connections.Given that the key LUT organs span multiple spatial scales and have complex interdependencies, we expect that the development of a model of LUT function will be a modular process in which models of individual LUT components , possibly developed by different research groups, will be connected to form a comprehensive model.lumped models and spatio-temporal models. Lumped models neglect spatial differences and instead focus on quantities that change with time. For example, although there are regions of low and high fluid velocity inside the bladder during voiding,8 many LUT models disregard them and consider only the net flow in and out of the bladder. As a result of these simplifying assumptions, the mathematical formulation of lumped models tends to be relatively simple and is amenable for analytical and numerical studies. Lumped models usually consist of ordinary differential equations (ODEs) that describe the time rate of change in physiological quantities. Spatio-temporal models, by contrast, do incorporate this spatial variation, which comes at the cost of increased mathematical complexity, which makes the analytic and numerical study of the model more challenging. Given that the connections between LUT components are quite complex, the mathematical simplicity of lumped models makes them a good starting point. However, it is unlikely that lumped models will be able to accurately describe all aspects of the behavior of the LUT . A base lumped model can then be enhanced by incorporating spatio-temporal elements and by the use of data-driven modeling techniques such as artificial neural networks.Most existing mathematical models of the LUT can roughly be classified in two model structures: To fix terminology that will be used throughout the review we define mechanistic and empirical models. Mechanistic models describe how the behavior of a physiological system emerges from the interactions of its constituent parts. Mechanistic models provide an understanding of the system’s internal workings (even when not directly observable) and can predict the system-level behavioral response to changes in its internal parts . Typically, however, the underlying physiological mechanisms are unknown, and we model only the observable behavior of the system instead of its constituent parts. These so-called phenomenological or empirical models give mathematical descriptions only of the correlations between observable variables. While empirical models can help us systematize the observable behavior of a system, they are constrained to variables that are experimentally accessible. Mechanistic descriptions of each LUT component are preferable for a comprehensive LUT model since explicitly describing each constituent part would create a common basis for different model components to communicate with each other. We may aim for use of mechanistic models to represent all aspects of LUT physiology but turn to the empirical models when a direct mechanism is unknown, as is common for models of LUT neural control.2 |Model references were found using keyword searches on Web of Science and PubMed related to the individual components of the LUT and mathematical models. References with models that capture connections between at least two LUT tissue-level components were retained and categorized as either a lumped or spatio-temporal model. Lumped models were prioritized for inclusion in the paper, whereas spatio-temporal models were evaluated in terms of how they may provide a more meaningful description of LUT physiology. Seminal models of LUT physiology were also included in our reference pool.9 This was an effort to assess what rules were typically followed before the publication codifying the TSR. We chose to highlight the following “Five simplest rules” (indicating what percentage of sources satisfied which criteria) which are reflective of what we feel are the minimum requirements to help promote reproducibility:Define clearly the context of the model—domain of intended use, clear prediction metrics (83.33%).Use contextually appropriate data for validation (4.17%).List assumptions and limitations of the model (75.00%).Version control using online repositories (0.00%).Model is documented appropriately with list of variables, parameters, values, and initial conditions (70.83%).In an effort to promote model credibility, accessibility and interoperability, references were also evaluated according to the Ten Simple Rules (TSR).References with models that met at least one of the five rules were included in the chord diagram .3 |10The bladder is a hollow organ that stores urine and periodically empties through the urethral outlet. Mathematical modeling of the bladder involves two distinct but interacting themes: fluid flow in and out (fluid dynamics) and behavior of the bladder walls during filling and voiding (biomechanics).VB changes with time.15 From the principle of conservation of mass and fixed fluid density, we obtain the relationQin denotes the net flow rate of fluid into the bladder  and Qout denotes the flow rate of urine from the bladder into the urethra. A model for Qin is proposed in Bastiaanssen et al.,11 which assumes that the peristaltic inflow of urine can be blocked by high pressure in the bladder:Pθ is a pressure threshold. The mathematical description of Qout is similar and will be discussed in The most common method for modeling the fluid dynamics of the bladder utilizes a lumped model that considers how its fluid volume 16 Different SLS models and variations, including the addition of an active element that models detrusor contraction, have been used in modeling the bladder.18 The biomechanics of the bladder is often simplified by representing the bladder as a spherical shell of thickness hB, internal radius RB, and internal fluid volume VB,19 as illustrated in TB of the bladder walls is proportional to the transmural pressure difference Δtrans,B = PB – Pabd, where PB and Pabd represent the pressures in the bladder and abdomen, respectively . Explicitly,The biomechanics of the bladder is concerned with the visco-elastic properties of its walls. Intuitively, as the bladder distends during filling, tension is generated in its walls. The standard linear solid model (SLS) represents this phenomenon via combinations of springs and dashpots.hB ≪ RB). Laplace law establishes a link between the fluid dynamics and biomechanics of the bladder, as it is an algebraic relation between fluid pressure and tension on the bladder walls. Indeed, the circumferential stretch of the bladder as urine flows in produces a tension increase in its walls. This tension increase is then related to the pressure inside the bladder via Laplace law assumes that the circumferential tension does not vary across the wall thickness, which is an appropriate assumption only when the shell is sufficiently thin , laminar (not turbulent), Newtonian, and through a rigid tube of circular cross-section, the following relation was derived from first principles by Hagen and Poiseuille nearly 200 years ago25 In the case of the urethra, for a one-dimensional (1-D) unsteady flow model, the balance of linear momentum yields the equation of motionρ is the mass density, PU is the fluid pressure in the urethra, u is the fluid velocity, x is the axial position, and t is time. From this equation, we see how changes in pressure produce changes in fluid velocity. From the balance of mass, we obtain the equationA are coupled to changes in fluid velocity. Finally, Ptrans,U = PU − Pabd is the transmural pressure drop , KP is proportional to the bending stiffness of the tube wall, and A0 the cross-sectional area when ΔPtrans = 0.The shape and structure of the urethra varies along its length such that consideration of position  may provide a more detailed analysis of flow.quation∂A∂t+∂∂x28 In more recent years, detailed anatomically-accurate representations of the urethra have been created based on histological and morphometric studies or using magnetic resonance imaging (MRI). For example, experimental and structural studies of the male urethra have been combined with 3-D finite element models to characterize urethral occlusions using artificial devices to provide urinary continence30; in addition, MRI studies of the female urethra have been combined with computationally intensive finite element models to simulate how contraction of striated and smooth muscles contribute to the urethral closure pressure (PC ).31 Allometric analyses of LUT function across animals with body mass ranging from 0.03 to 8000kg find urethral length, diameter, bladder capacity, and steady-state flow rate scale with body mass, but bladder pressure and duration of urination are relatively insensitive to body size.32 Moreover, such analyses suggest that small-rodent studies guiding models of the human LUT may need to be adapted to put less attention on viscous and capillary forces and more attention on the role of hydrostatic pressure.Of course, the urethra does not have a simple tube-like geometry and interacts biomechanically with both the internal and external urethral sphincters differently across its length and in three dimensions (3-D). In exploring more complex urethral flow regimes, 2- and 3-D models were designed for which analytical and numerical results have been developed.5 |35 some focus on the behavior of specific neurons,36 and others create elaborate controllers to mimic the central integration of signals.18 In empirical models,35 pressure is the typical driving mechanical stimulus used because it is highly correlated with distension 37 in most physiological states and is readily accessible experimentally. The le Feber approach utilizes a piecewise-defined function to model changes in pressure and pudendal sensory nerve activity based on the onset and duration of externally infused urethral fluid - an approach that generates excellent fits to their original data but limits its predictive validity beyond the protocol in which it was fit.34 To facilitate protocol-independent prediction, Danziger and Grill proposed a more general, autonomous model of the sensory pudendal nerve that uses pressure P(t), its time rate of change, and the nerve activity s to approximate the time rate of change of neural activity35:k is a time constant, and t is time. The transduction functions f0 and f1 map P(t) and 35While the bladder and urethra are usually idealized as a sphere and a collapsible tube, the neural analog is electric circuitry. These circuits transduce mechanical work into voltage (for sensory neurons) or vice versa (for motor neurons). Neural models come in a range of modalities - some use empirically linked circuit outputs to LUT fluid dynamics,38 This allows isolation of these fiber types for easier model validation via unambiguous electrophysio-logical recordings. This is a luxury not afforded to the pelvic nerve where mixed fiber types travel in the same fascicles. Attempts to dissociate the afferent and efferent activity of the pelvic nerve through different assumptions or experimental design are difficult to validate - though linear separability of mixed nerve activity has been postulated.33The pudendal nerve was likely the first focus of bulk nerve modeling because its neuroanatomical organization permits physical separation of the sensory and motor fibers.39 The McGee model provides a plausible heuristic for the integration of neural activity in the spinal cord, which was previously modeled with neurophysiologically uninformed control systems.40 Similar to the Danziger model, the McGee model uses pressure directly to compute its predictions of neural activation, allowing it to be combined straightforwardly with previously described biomechanics models. Conversely, Hodgkin–Huxley model neurons operate at cellular and intracellular scales to evaluate ion channel dynamics, often impairing computational tractability with organ-scale models.36Models of individual neurons are sparse in LUT literature, and each uses different approaches. Linear integrate and fire (LIF) neurons were used to construct the neural network associated with the pudendo-vesical reflex in 41 In each phase, for instance, the bladder and sphincter muscles have coordinated, reciprocal activation patterns to promote the current phase of the system. Consequently, modelers have used this analogy to create control models utilizing threshold-gated circuits to switch between filling and voiding. What started out as a single switch controller,40 has evolved to better represent  the neuroanatomical organization of LUT controllers in the central nervous system , where more connections were used and specific details about individual neurons were incorporated into their design .41 The advantage to the de Groat model compared to the other control models,18 lies in the modularity of the model’s neurons; however the model is more focused on conceptually demonstrating that a switching circuit could be constructed from known neuron types rather than elaborating a physiologically accurate connectome. Despite the ability of control models to produce nominal behavioral profiles accurately , there are no physiological quantities with which models of disease and therapies can interface. Though therapeutic modeling is not currently prevalent in LUT literature, an important step towards ensuring that it can be a possibility is to use quantitative physiological inputs and outputs derived from mechanistic representations of the central neural connections. For this reason, a shift toward mechanistic representations and away from control models is warranted.The central coordination of neural signals from the LUT has often been described as a switching mechanism to change between phases of filling and voiding.s system .13 The d6 |43 We developed a chord diagram in Due to its complexity, the development of a functionally predictive systems physiology model of the LUT will likely have to be a modular process in which independently designed models of individual components, such as the ones surveyed thus far, will fit together in a unified model. Toward this goal, a diagram of existing LUT component models and their interconnectivity can clarify the state of the field, establish a common conceptual framework, help investigators put their models in the context of larger modeling efforts, encourage interoperability, and identify major knowledge gaps.13 and Perez et al.17 On the other hand, spatio-temporal models, despite the precision with which they can represent certain phenomena, tend to fail to simulate both phases. For example, the spatio-temporal model of the bladder proposed by Tziannaros et al.8 can predict with great detail the fluid dynamics of the voiding phase, while the filling phase is absent from the model.47There are technical considerations that might make certain models more amenable for a modular and collaborative project of this sort. Perhaps the most fundamental aspect of LUT function is the fact that it occurs in cycles, each consisting of two phases: filling and voiding. For different models to operate together, their dynamics should be able to reproduce the cyclic behavior of the LUT. Lumped models have so far been more successful in simulating cycles, as in Paya et al.9 to improve credibility, accessibility and inter-operability. Adherence to the TSR, especially open-source test data and online repositories of model code, is fundamental to the success of a collaborative and long-term project.A field-wide LUT modeling effort across multiple independent investigators necessitates a set of standard good practices to help facilitate the integration of new models into the modular framework, such as those suggested in Erdemir et al.50 It is important that these smaller-scale models consider how they might connect with the larger components for inclusion into the comprehensive model by having a unifying physiological interaction (such as tension49) between anatomical levels.To progress towards a predictive organ-scale model, models of neural interactions with LUT organs will need to be more physiologically motivated and conform better to good modeling practices in the future, mirroring work on bladder biomechanics models. But a significant amount of modeling efforts and physiological exploration are still necessary to achieve this goal. There also is motivation to explore smaller physiological scales which will be a crucial step towards predicting the propagation of cellular-level effects from, for example, pharmacological interventions.7 |Even though a comprehensive, functionally predictive model of the LUT is presently unavailable, a significant body of work modeling different components of the LUT has been developed over several decades. We have surveyed models of the three main components of the LUT: bladder, urethra, and neural control. In the case of the bladder and the urethra, we have privileged lumped models as their simplicity makes them the best option, at least initially, for incorporation into a unified model. The models we have reviewed should not be regarded as finalized projects, but rather as stepping stones for the next challenges that lie ahead. From our perspective, the most urgent of which is to create new mathematical descriptions of the neural control of the LUT with a view towards developing a comprehensive model of LUT function. It appears to us that the field is ready to systematically undertake this challenge.