Effect of hypobaric hypoxia, simulating conditions during long-haul air travel, on coagulation, fibrinolysis, platelet function, and endothelial activation

CONTEXT: The link between long-haul air travel and venous thromboembolism is the subject of continuing debate. It remains unclear whether the reduced cabin pressure and oxygen tension in the airplane cabin create an increased risk compared with seated immobility at ground level. OBJECTIVE: To determine whether hypobaric hypoxia, which may be encountered during air travel, activates hemostasis. DESIGN, SETTING, AND PARTICIPANTS: A single-blind, crossover study, performed in a hypobaric chamber, to assess the effect of an 8-hour seated exposure to hypobaric hypoxia on hemostasis in 73 healthy volunteers, which was conducted in the United Kingdom from September 2003 to November 2005. Participants were screened for factor V Leiden G1691A and prothrombin G20210A mutation and were excluded if they tested positive. Blood was drawn before and after exposure to assess activation of hemostasis. INTERVENTIONS: Individuals were exposed alternately (> or =1 week apart) to hypobaric hypoxia, similar to the conditions of reduced cabin pressure during commercial air travel (equivalent to atmospheric pressure at an altitude of 2438 m), and normobaric normoxia (control condition; equivalent to atmospheric conditions at ground level, circa 70 m above sea level). MAIN OUTCOME MEASURES: Comparative changes in markers of coagulation activation, fibrinolysis, platelet activation, and endothelial cell activation. RESULTS: Changes were observed in some hemostatic markers during the normobaric exposure, attributed to prolonged sitting and circadian variation. However, there were no significant differences between the changes in the hypobaric and the normobaric exposures. For example, the median difference in change between the hypobaric and normobaric exposure was 0 ng/mL for thrombin-antithrombin complex (95% CI, -0.30 to 0.30 ng/mL); -0.02 [corrected] nmol/L for prothrombin fragment 1 + 2 (95% CI, -0.03 to 0.01 nmol/L); 1.38 ng/mL for D-dimer (95% CI, -3.63 to 9.72 ng/mL); and -2.00% for endogenous thrombin potential (95% CI, -4.00% to 1.00%). CONCLUSION: Our findings do not support the hypothesis that hypobaric hypoxia, of the degree that might be encountered during long-haul air travel, is associated with prothrombotic alterations in the hemostatic system in healthy individuals at low risk of venous thromboembolism.

Official warnings on thromboembolism risk with oral contraceptives fail to inform users adequately

Following the 1995 “pill scare” relating to the risk of venous thrombosis from taking second- or third-generation oral contraceptives, the Committee on Safety of Medicines (CSM) withdrew their earlier recommended restrictions on the use of third-generation pills and published recommended wording to be used in patient information leaflets. However, the effectiveness of this wording has not been tested. An empirical study (with 186 pill users, past users, and non-users) was conducted to assess understanding, based on this wording, of the absolute and relative risk of thrombosis in pill users and in pregnancy. The results showed that less than 12% of women in the (higher education) group fully understood the absolute levels of risk from taking the pill and from being pregnant. Relative risk was also poorly understood, with less than 40% of participants showing full understanding, and 20% showing no understanding. We recommend that the CSM revisit the wording currently provided to millions of women in the UK.

Inverse model control using recurrent networks

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been produced, a controller can be produced which consists of the network comprising the inverse of the model and a filter. Thus, the network providing control for the nonlinear system does not require any training after it has been trained to model the nonlinear system. Stability and other issues of importance for nonlinear control systems are also discussed.

Input/output linearization using dynamic recurrent neural networks

This paper brings together two areas of research that have received considerable attention during the last years, namely feedback linearization and neural networks. A proposition that guarantees the Input/Output (I/O) linearization of nonlinear control affine systems with Dynamic Recurrent Neural Networks (DRNNs) is formulated and proved. The proposition and the linearization procedure are illustrated with the simulation of a single link manipulator.

The relative order and inverses of recurrent networks

Differential geometry is used to investigate the structure of neural-network-based control systems. The key aspect is relative order—an invariant property of dynamic systems. Finite relative order allows the specification of a minimal architecture for a recurrent network. Any system with finite relative order has a left inverse. It is shown that a recurrent network with finite relative order has a local inverse that is also a recurrent network with the same weights. The results have implications for the use of recurrent networks in the inverse-model-based control of nonlinear systems.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

Stable receding horizon control based on recurrent networks

The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks

This work provides a framework for the approximation of a dynamic system of the form x˙=f(x)+g(x)u by dynamic recurrent neural network. This extends previous work in which approximate realisation of autonomous dynamic systems was proven. Given certain conditions, the first p output neural units of a dynamic n-dimensional neural model approximate at a desired proximity a p-dimensional dynamic system with n>p. The neural architecture studied is then successfully implemented in a nonlinear multivariable system identification case study.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

A practical approach for training dynamic recurrent neural networks: use of Apriori information

Presents a technique for incorporating a priori knowledge from a state space system into a neural network training algorithm. The training algorithm considered is that of chemotaxis and the networks being trained are recurrent neural networks. Incorporation of the a priori knowledge ensures that the resultant network has behaviour similar to the system which it is modelling.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

Dynamic input/output linearization using recurrent neural networks

A dynamic recurrent neural network (DRNN) is used to input/output linearize a control affine system in the globally linearizing control (GLC) structure. The network is trained as a part of a closed loop that involves a PI controller, the goal is to use the network, as a dynamic feedback, to cancel the nonlinear terms of the plant. The stability of the configuration is guarantee if the network and the plant are asymptotically stable and the linearizing input is bounded.

Identification of nonlinear systems with a dynamic recurrent neural network

Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) in order to identify the input-output dynamics of a class of nonlinear systems. The number of states of the identified network is constrained to be the same as the number of states of the plant.

Relative order defines a topology for recurrent networks

This paper uses techniques from control theory in the analysis of trained recurrent neural networks. Differential geometry is used as a framework, which allows the concept of relative order to be applied to neural networks. Any system possessing finite relative order has a left-inverse. Any recurrent network with finite relative order also has an inverse, which is shown to be a recurrent network.