Pivotal Measures in Statistical Experiments and Sufficiency


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Animals exposed to 3 weeks of hypoxia were returned to normoxia for an additional 2, 5, or 9 weeks. Horizontal lines represent the mean shear modulus of each group, and each symbol corresponds to 1 individual PA. Increased matrix stiffness leads to decreased COX-2 expression and prostanoid synthesis. To elucidate the mechanism underlying stiffness-dependent vascular cell activation, we performed a PCR array for a panel of hypertension genes and discovered several genes that were differentially regulated by stiffness.

Increased matrix stiffness upregulated several vasoconstrictors e.

Statistical inference

Given our prior work demonstrating enhanced contractility in COX-2—deficient PASMC 39 , we hypothesized that matrix stiffness—induced COX-2 suppression might drive stiffness-dependent remodeling behaviors. Increased matrix stiffness leads to decreased COX-2 expression and prostanoid production. We next assessed whether increases in stiffness alter autocrine levels of COX-2—derived prostaglandins.

In addition, we analyzed temporal expression of COX-2—derived prostanoids in lung tissue by metabololipidomics in experimental PH. To test the efficacy of this strategy in vivo, we also examined whether exogenous treprostinil, a long-acting prostacyclin analog, would inhibit PA stiffening in vivo in experimental PH. This reduction in PA stiffness correlated with significant decreases in collagen and fibronectin expression Figure 7, C—F. Representative traction fields following treatment with vehicle row 1, M — O or iloprost row 2, P — R.

Each column represents discrete substrate stiffness. Color scale indicates magnitude of traction in kPa. Sprague-Dawley rats were treated with MCT or vehicle and, after 2 weeks, had s. Sprague-Dawley rats were treated with monocrotaline MCT or vehicle and, after 2 weeks, were treated with i. Taken together, our findings demonstrate that alterations in the mechanical environment play a key role in triggering a proremodeling program and suggest that stiffness-dependent COX-2 suppression and reduction in prostanoid production are critical to the emergence of the matrix stiffness—driven cellular phenotype in vitro, along with the distal vascular stiffening and subsequent RV dysfunction observed in PH models in vivo.

In this study, we used AFM microindentation to mechanically characterize the stiffness of PAs at the micron scale in human PAH and experimental PH and investigated the role of matrix stiffness in the pathogenesis of PH. Our results highlight several important new concepts. First, early PA stiffening originates distally, propagates proximally, and precedes hemodynamic alterations in the evolution of experimental PH.

Our findings suggest that increases in PA stiffness are not merely sequelae of end-stage disease, but rather that distal vascular matrix stiffening and its proximal propagation trigger critical cellular phenotypic changes that promote progressive vascular remodeling in an adverse COX-2—dependent mechanobiological feedback loop.

Recent studies demonstrate that PA stiffness is associated with increased mortality in PAH patients 5 — 7 and may be a better predictor of functional status 8 , 11 than traditional clinical parameters. Several modalities to estimate PA stiffness in patients have been developed, including right heart catheterization 7 , intravascular ultrasound 6 , 48 , cardiac magnetic resonance CMR imaging 5 , 10 , 11 , 49 , and echocardiography 8 , 50 , In animal models, stiffness measurements have focused on isolated vessel mechanical testing 15 — 17 , 19 — 22 , pressure-flow measurements 18 , and — more recently — Doppler imaging However, clinical studies 5 , 7 , 8 , 11 , 50 , 51 and animal models 15 , 16 , 19 , 22 , 52 have primarily focused on PA stiffness measured at the macroscopic scale in established disease.

In clinical studies, large vessel stiffening measured at the macroscopic scale has been shown to contribute to increased RV workload, as well as to predict clinical outcomes in PAH 8 , 9. In animal models, large vessel stiffening following exposure to hypoxia has been shown to correlate with increases in vessel wall elastin 16 , 22 and collagen 15 , 16 , 19 , as well as increased collagen cross-linking Although considerable progress has been made in our understanding of large vessel stiffening at the macroscopic level, little is known about small vessel stiffening measured at the microscopic level and how micromechanical changes may influence resident vascular cells to promote the development and progression of PH.

Using AFM microindentation, our study demonstrates for the first time to our knowledge that PAs develop dramatic mechanical changes at the micron scale in human PAH and experimental models of PH. We also report for the first time to our knowledge that increased PA stiffness occurs early prior to the development of other PH phenotypes and that inhibition of early PA stiffening prevents the development of PH. While measurements of tissue stiffness at the microscopic scale are fundamentally different than traditional measures of PA stiffness at the macroscopic scale and are not sufficient to generate detectable hemodynamic changes, our findings suggest that early changes in the local mechanical environment contribute to progressive vascular remodeling and promote the development of PH.

Although recent work suggests that early increases in LOX expression and collagen cross-linking contribute to PH pathogenesis 23 , our findings now provide evidence that vessel wall stiffening is a pivotal early event that propagates pulmonary vascular remodeling and drives progression of PH.

The mechanisms underlying PA elasticity changes and how they contribute to PH pathogenesis are incompletely understood. Prior attention has focused predominantly on flow pulsatility in large vessels and perturbations in shear stress that contribute to remodeling and regulation of vascular tone 24 , Collectively, our findings suggest that increases in PA stiffness trigger a cellular remodeling program that leads to progressive matrix stiffening and amplification of remodeling in an adverse self-sustaining feedback loop. We found that exogenous iloprost can prevent the stiffness-driven shift to a remodeling phenotype in PASMC and can restore cellular behaviors similar to those observed on matrices of physiologic PA stiffness.

Importantly, our results also revealed that early administration of treprostinil in vivo at the initiation of PA stiffening, but prior to hemodynamic alterations and development of RVH, completely prevents MCT-induced PH.


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Future studies will focus on mechanotransduction and downstream prostaglandin receptors, given the differential responses of individual prostacyclin analogs on prostanoid receptors 54 — Endogenous levels of prostacyclin are reduced in patients with PAH 41 and exogenous prostanoid therapy remains a cornerstone for the treatment of PAH Moreover, recent data from the REVEAL registry suggest that prostanoid therapy is not consistently being prescribed in patients with severe PAH 60 , despite current guideline recommendations Our results provide an explanation for the reduced prostacyclin levels in PAH patients and suggest that early vascular stiffening and suppression of endogenous prostanoid levels underlie the mechanoactivation of vascular cells and disease progression in PAH.

Furthermore, our findings have important clinical implications and argue for initiation of prostanoid therapy early in the disease course to interrupt mechanopathobiologic signaling from the matrix to resident vascular cells and arrest progression of vascular remodeling. This supports emerging evidence that PAH patients with less severe disease may benefit from prostanoids 61 , 62 , a treatment strategy that now may be more feasible with the recent FDA approval of oral prostacyclin analogs 63 , To gain new insight into micromechanical remodeling in PH, we applied AFM microindentation to measure PA stiffness at the cellular scale within the intact native lung architecture 38 , Although AFM is invasive, it allowed us to assess the mechanical properties of intrapulmonary vessels with micron-scale spatial resolution.

There are clear advantages to our approach, including high spatial resolution, small deformations, and sensitivity to modest changes in mechanical properties. However, AFM is not without limitations. Sample preparation for AFM characterization requires sectioning of unfixed tissue; thus, the boundary conditions for vessels and surrounding parenchyma are modified from their in situ state.

Nevertheless, AFM has been used previously to characterize remodeling-associated changes in vascular wall stiffness using similar approaches 12 , As our AFM measurements were made at room temperature in nonperfused tissue, the measured tissue stiffness does not take into account active cellular contributions, as well as possible contributions from changes in transmural distending pressure 67 or vascular flow.

However, as this study focused on distal PA stiffness, we suspect that flow and transmural pressure contributions to stiffness are small and that our measurements capture changes in vascular wall stiffness associated with ECM remodeling Additional limitations of AFM include sampling challenges associated with the small spatial scale of measurements and inherent biological heterogeneity across and within vessels.

In human samples, we observed smaller differences in PA stiffness between control and PAH patients than differences observed in our animal PH models. Potential explanations for this difference in magnitude include lack of OCT inflation in human lungs that may have affected AFM measurements, heterogeneity of disease subtype and severity in PAH patients, and demographics of human subjects.

Furthermore, treatment with prostanoids and other medications at the time of lung transplantation may have confounded stiffness assessment in human PAH tissue. In addition, control samples were procured from donors with inadequate organ quality who may have had unknown pulmonary vascular elasticity changes. Despite these challenges, we observed statistically significant alterations in vessel stiffness in both human PAH and experimental models of PH, emphasizing the robust nature of distal vascular stiffening in PH.

In summary, we demonstrate that under conditions of pathologic PA stiffness, suppression of COX-2 expression and PGI 2 synthesis triggers a key shift in cellular behaviors to a remodeling phenotype with enhanced proliferation, matrix deposition, and contractility, leading to further increases in arterial stiffness and amplification of remodeling.

Furthermore, our findings suggest that, once increases in PA stiffness are established, propagation of the vascular remodeling process is self-sustaining via activation of PASMC and PAEC and attenuation of COX-2 expression in a mechanobiological feedback loop. Although long regarded as a consequence of vascular remodeling, our results now demonstrate that increases in PA stiffness contribute to the pathogenesis of PH and suggest that interrupting this stiffness-dependent feedback cycle may offer a novel therapeutic approach for the treatment of patients with PH.

Iloprost was obtained from Cayman Chemical. All cell culture reagents were obtained from Lonza unless otherwise specified. Treprostinil was provided by United Therapeutics.

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All other reagents were obtained from Sigma-Aldrich unless otherwise specified. Animals were obtained via heterozygous breeding, and genotypes were determined by a PCR protocol provided by the Jackson Laboratory with modification. MCT model. Sugen hypoxia model. Adult male Sprague-Dawley rats weighing — g were injected s.

Following 3 weeks of hypoxia, subgroups of animals were reexposed to normoxia for 2, 5, or 9 weeks as described 43 , 44 , for a total of 5, 8, or 12 weeks following SU or vehicle. Five and 8 weeks after SU 3 weeks of hypoxia followed by 2 or 5 weeks of normoxia , rats were anesthetized with isoflurane and echocardiography performed using a Vevo high-resolution microultrasound system VisualSonics and a MHz probe.

Vessels occluded with blood were excluded. The cantilever spring constant was further confirmed by the thermal fluctuation method For each animal, at least 3 slides per animal were analyzed. Share This Paper.

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Citations Publications citing this paper. Estimation and orthogonal block structure Sandra S. Author ' s personal copy Tiago Mexia , Manuela M. References Publications referenced by this paper. Fonseca , J. Mexia , R. This can be changed by modifying a BOSH property , however Pivotal does not recommend increasing it beyond Monitoring the number of currently running containers on your cells is important for understanding overall deployment usage. Checking how many additional containers the cell can run can help indicate whether or not you need to scale out your deployment.

Note that PCF Healthwatch provides the additional metric healthwatch. Insufficient cell memory can make it impossible to scale existing applications or push new ones. Monitoring these metrics will help you understand if your deployment has sufficient resources. When monitoring remaining memory aggregated across all cells, PCF recommends setting a warning threshold of around 64 GB and a critical alert threshold of 32 GB. One important thing to note is that when a user assigns resources to an application, Diego allocates those resources assuming the workload will require all of them at some point.

In other words, the remaining memory metric will reflect what Diego has allocated as potentially needed, rather than the amount of memory that is actually in use at the moment the metric is reported. Note that PCF Healthwatch generates two additional metrics to help monitor cell memory. The Healthwatch metric healthwatch. AvailableFreeChunks reports the number of available chunks of memory remaining across all cells, providing a quick understanding of remaining capacity.

This metric is most useful after determining the footprint of a standard application on your deployment. Healthwatch also reports the metric healthwatch.

Key metrics for monitoring Pivotal Cloud Foundry | Datadog

Monitoring how much disk space cells have available can provide insight into whether staging tasks are likely to start failing, as well as whether the deployment needs larger or more cells. The standard amount of disk capacity needed to ensure staging tasks do not fail is 6 GB, so PCF recommends setting a warning threshold of 12 GB and a critical alert threshold of 6 GB. As with memory, Diego reports remaining disk space based on the resources that have been assigned to currently running tasks, rather than the resources that are actually in use at the moment that the metric is reported.

Note that PCF Healthwatch generates the additional metric healthwatch. PCF recommends alerting if this metric falls below about 35 percent. If the Rep begins to report high sync duration times, the slowdown might point to problems with the BBS. PCF recommends setting a warning threshold of five seconds and a critical alert threshold of around 10 seconds for this metric. Correct routing tables are necessary for the Gorouter to monitor which application instances are available so it can route traffic appropriately.

The Route-Emitter on each cell sends an update to the BBS whenever there are any changes in registered routes, and it also periodically sends an up-to-date image of the full routing table. Monitoring the total number of routes in the routing table can give you a sense of deployment scale and can also alert you to significant and rapid changes, such as many routes suddenly becoming unregistered.

The Gorouter depends on the routing tables sent by the Route-Emitter, so discrepancies or dropped routes would leave the Gorouter unable to shuttle traffic correctly. PCF recommends alerting if the average number of registration messages received by the Gorouters falls below the total number of messages sent across all Route-Emitters.

Loggregator is a system of components that aggregates and streams application logs and deployment metrics. As part of this process, Loggregator standardizes and packages these messages as envelopes based on their type to make it easier for downstream components to sort and parse them. The primary components of Loggregator to monitor are the Metron Agents, which sit on both infrastructure and host VMs and forward messages; the Doppler servers, which aggregate messages from all Metrons and store them in buffers based on envelope type; and Traffic Controllers, which consume messages from Dopplers and stream them via the Firehose.

Alternatively, operators can choose to bind a syslog drain to applications, in which case messages go from the Dopplers to a Reverse Log Proxy and then on to a Syslog Adapter. From there, the logs are streamed via rsyslog to the specified drain endpoint. The volume of messages that the Loggregator system can process depends on how quickly each downstream component can consume the information. If consumers cannot keep up with the data being sent from sources, messages will simply be dropped to make room for the next batch. This means that each individual step in the Loggregator chain provides a possible breaking point.

In particular, message loss can result if the message buffer gets backed up at the Doppler layer, as the Dopplers receive messages from multiple Metron Agents. Individual components within Loggregator emit metrics tracking the volume of messages sent and received. For example, the Doppler metric doppler. The following component metrics provide information about how many messages are being received or dropped by various components within the Loggregator chain:.

The number of messages sent through Loggregator, and even the number that are dropped without delivery, can be largely dependent on the size of the deployment. In order to get a clearer, more at-a-glance indicator of Firehose performance, it is helpful to use these metrics to calculate rates of message throughput and loss. PCF Healthwatch derives several metrics that give operators insight into the performance and capacity of their Loggregator system.

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The following are Healthwatch metrics that use raw component metrics to derive rates of message loss across various Loggregator layers, making it easier to observe changes in Loggregator throughput and performance. This metric reports the rate of message loss as messages pass through the Doppler servers by comparing the number of messages the Dopplers receive with the number of messages that the Dopplers drop without forwarding.

In other words, the metric is derived from loggregator. PCF suggests setting a warning threshold of 0. Increases in the rate of message loss can indicate that you need to scale up your Doppler or Traffic Controller instances to process the messages effectively. This metric provides the average number of messages that each Doppler instance is processing per minute. It is derived by dividing the sum of all messages ingested by all Dopplers by the number of Doppler servers. This metric indicates whether your deployment needs more Doppler servers to handle the number of log messages being processed.

Correlating this with healthwatch. PCF recommends setting an alert threshold of 16, messages per second, or one million messages per minute. At this point, you will likely need to scale the number of Doppler instances to avoid message loss. This metric represents the number of syslog drains divided by the number of Syslog Adapters i.

Each Syslog Adapter can support around drain bindings. The metric cf-syslog-drain. Dividing that across the number of available Syslog Adapters clearly indicates whether the deployment needs more adapters to support the number of drain bindings. These metrics are derived by loggregator. This pair of metrics are applicable only to application logs generated by applications that have a syslog drain bound to them and do not reflect the status of the Firehose. These derived metrics provide insight into the percentage of message loss per minute at the Reverse Log Proxy layer and at the Syslog Adapter layer.

In both cases, PCF suggests setting a warning threshold at a rate of 0. High rates of syslog drain log loss can indicate performance problems with your syslog server or endpoint. Aside from the metrics mentioned above, there are several additional PCF Healthwatch metrics that can be helpful for monitoring overall deployment health.

The most important available health checks for cf CLI commands are for login , push , start , logs , stop , and delete.

These status checks perform smoke tests to confirm whether these fundamental commands are working so that developers can push and manage applications. Any failed health checks for these commands indicate that your platform is missing key functionality that is needed for normal operations. By default, the Apps Manager acts as a canary app. That is, Healthwatch queries whether it is up and running as a test of overall application health. Its failure can be an indicator that other staged applications might be encountering performance or availability problems.

Pivotal Cloud Foundry is a complex, distributed system that abstracts underlying infrastructure so that developers can focus on code. It provides a scalable and highly available platform for developers to simply push an application and have it be available within minutes. In this post, we have taken a look at several key metrics that can give PCF operators insight into the utilization and health of their overall deployment, as well as the status of the components that make up that deployment. These metrics are invaluable for tracking not only current performance but also for determining whether it is necessary to scale your deployment to ensure sufficient resources are available for developers and end users.

In part three of this series, we will demonstrate several methods of collecting PCF metrics and logs. We will cover both how to tap the Firehose stream of deployment metrics and application logs, and how to utilize a syslog drain to view component system logs. New announcements from Dash! Toggle navigation. Part 1: Pivotal Cloud Foundry architecture.

Pivotal Measures in Statistical Experiments and Sufficiency Pivotal Measures in Statistical Experiments and Sufficiency
Pivotal Measures in Statistical Experiments and Sufficiency Pivotal Measures in Statistical Experiments and Sufficiency
Pivotal Measures in Statistical Experiments and Sufficiency Pivotal Measures in Statistical Experiments and Sufficiency
Pivotal Measures in Statistical Experiments and Sufficiency Pivotal Measures in Statistical Experiments and Sufficiency
Pivotal Measures in Statistical Experiments and Sufficiency Pivotal Measures in Statistical Experiments and Sufficiency
Pivotal Measures in Statistical Experiments and Sufficiency Pivotal Measures in Statistical Experiments and Sufficiency
Pivotal Measures in Statistical Experiments and Sufficiency Pivotal Measures in Statistical Experiments and Sufficiency
Pivotal Measures in Statistical Experiments and Sufficiency Pivotal Measures in Statistical Experiments and Sufficiency
Pivotal Measures in Statistical Experiments and Sufficiency

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