Effects of proportional assisted ventilation on exercise performance in idiopathic pulmonary fibrosis patients

Background: Patients with idiopathic pulmonary fibrosis (IPF) present an important ventilatory (imitation reducing their exercise capacity. Non-invasive ventilatory support has been shown to improve exercise capacity in patients with obstructive diseases; however, its effect on IPF patients remains unknown. Objective: The present study assessed the effect of ventilatory support using proportional, assist ventilation (PAV) on exercise capacity in patients with IPF. Methods: Ten patients (61.2 +/- 9.2 year-old) were submitted to a cardiopulmonary exercise testing, plethysmography and three submaximal. exercise tests (60% of maximum load): without ventilatory support, with continuous positive airway pressure (CPAP) and PAV. Submaximal tests were performed randomly and exercise capacity, cardiovascular and ventilatory response as well as breathlessness subjective perception were evaluated. Lactate plasmatic levels were obtained before and after submaximal. exercise. Results: Our data show that patients presented a limited exercise capacity (9.7 +/- 3.8 mL O(2)/kg/min). Submaximal. test was increased in patients with PAV compared with CPAP and without ventilatory support (respectively, 11.1 +/- 8.8 min, 5.6 +/- 4.7 and 4.5 +/- 3.8 min; p < 0.05). An improved arterial oxygenation and lower subjective perception to effort was also observed in patients with IPF when exercise was performed with PAV (p < 0.05). IPF patients performing submaximal exercise with PAV also presented a lower heart rate during exercise, although systolic and diastolic pressures were not different among submaximal tests. Our results suggest that PAV can increase exercise tolerance and decrease dyspnoea and cardiac effort in patients with idiopathic pulmonary fibrosis. (C) 2009 Elsevier Ltd. All rights reserved.

Bilevel vs ICU Ventilators Providing Noninvasive Ventilation: Effect of System Leaks A COPD Lung Model Comparison

Background: Noninvasive positive-pressure ventilation (NPPV) modes are currently available on bilevel and ICU ventilators. However, little data comparing the performance of the NPPV modes on these ventilators are available. Methods: In an experimental bench study, the ability of nine ICU ventilators to function in the presence of leaks was compared with a bilevel ventilator using the IngMar ASL5000 lung simulator (IngMar Medical; Pittsburgh, PA) set at a compliance of 60 mL/cm H(2)O, an inspiratory resistance of 10 cm H(2)O/L/s, an expiratory resistance of 20 cm H(2)O/L/s, and a respiratory rate of 15 breaths/min. All of the ventilators were set at 12 cm H(2)O pressure support and 5 cm H(2)O positive end-expiratory pressure. The data were collected at baseline and at three customized leaks. Main results: At baseline, all of the ventilators were able to deliver adequate tidal volumes, to maintain airway pressure, and to synchronize with the simulator, without missed efforts or auto-triggering. As the leak was increased, all of the ventilators (except the Vision [Respironics; Murrysville, PA] and Servo I [Maquet; Solna, Sweden]) needed adjustment of sensitivity or cycling criteria to maintain adequate ventilation, and some transitioned to backup ventilation. Significant differences in triggering and cycling were observed between the Servo I and the Vision ventilators. Conclusions: The Vision and Servo I were the only ventilators that required no adjustments as they adapted to increasing leaks. There were differences in performance between these two ventilators, although the clinical significance of these differences is unclear. Clinicians should be aware that in the presence of leaks, most ICU ventilators require adjustments to maintain an adequate tidal volume. (CHEST 2009; 136:448-456)

Adjuncts to Physical Training of Patients With Severe COPD: Oxygen or Noninvasive Ventilation?

BACKGROUND: Previous studies have shown positive effects from noninvasive ventilation (NIV) or supplemental oxygen on exercise capacity in patients with COPD. However, the best adjunct for promoting physiologic adaptations to physical training in patients with severe COPD remains to be investigated. METHODS: Twenty-eight patients (mean +/- SD age 68 +/- 7 y) with stable COPD (FEV(1) 34 +/- 9% of predicted) undergoing an exercise training program were randomized to either NIV (n = 14) or supplemental oxygen (n = 14) during group training to maintain peripheral oxygen saturation (S(pO2)) >= 90%. Physical training consisted of treadmill walking (at 70% of maximal speed) 3 times a week, for 6 weeks. Patients were assessed at baseline and after 6 weeks. Assessments included physiological adaptations during incremental exercise testing (ratio of lactate concentration to walk speed, oxygen uptake [(V) over dot(O2)], and dyspnea), exercise tolerance during 6-min walk test, leg fatigue, maximum inspiratory pressure, and health-related quality of life. RESULTS: Two patients in each group dropped out due to COPD exacerbations and lack of exercise program adherence, and 24 completed the training program. Both groups improved 6-min walk distance, symptoms, and health-related quality of life. However, there were significant differences between the NIV and supplemental-oxygen groups in lactate/speed ratio (33% vs -4%), maximum inspiratory pressure (80% vs 23%), 6-min walk distance (122 m vs 47 m), and leg fatigue (25% vs 11%). In addition, changes in S(pO2)/speed, (V) over dot(O2), and dyspnea were greater with NIV than with supplemental-oxygen. CONCLUSIONS: NIV alone is better than supplemental oxygen alone in promoting beneficial physiologic adaptations to physical exercise in patients with severe COPD.

High CO(2)/H(+) dialysis in the caudal ventrolateral medulla (Loeschcke`s area) increases ventilation in wakefulness

Central chemoreception, the detection of CO(2)/H(+) within the brain and the resultant effect on ventilation, was initially localized at two areas on the ventrolateral medulla, one rostral (rVLM-Mitchell`s) the other caudal (cVLM-Loeschcke`s), by surface application of acidic solutions in anesthetized animals. Focal dialysis of a high CO(2)/H(+) artificial cerebrospinal fluid (aCSF) that produced a milder local pH change in unanesthetized rats (like that with a similar to 6.6 mm Hg increase in arterial P(CO2)) delineated putative chemoreceptor regions for the rVLM at the retrotrapezoid nucleus and the rostral medullary raphe that function predominantly in wakefulness and sleep, respectively. Here we ask if chemoreception in the cVLM can be detected by mild focal stimulation and if it functions in a state dependent manner. At responsive sites just beneath Loeschcke`s area, ventilation was increased by, on average, 17% (P < 0.01) only in wakefulness. These data support our hypothesis that central chemoreception is a distributed property with some sites functioning in a state dependent manner. (C) 2010 Elsevier B.V. All rights reserved.

Focal CO2 dialysis in raphe obscurus does not stimulate ventilation but enhances the response to focal CO2 dialysis in the retrotrapezoid nucleus

Simultaneous inhibition of the retrotrapezoid nucleus (RTN) and raphe obscurus (ROb) decreased the systemic CO2 response by 51%, an effect greater than inhibition of RTN (- 24%) or ROb (0%) alone, suggesting that ROb modulates chemoreception by interaction with the RTN (19). We investigated this interaction further by simultaneous dialysis of artificial cerebrospinal fluid equilibrated with 25% CO2 in two probes located in or adjacent to the RTN and ROb in conscious adult male rats. Ventilation was measured in a whole body plethysmograph at 30 C. There were four groups (n = 5): 1) probes correctly placed in both RTN and ROb (RTN-ROb); 2) one probe correctly placed in RTN and one incorrectly placed in areas adjacent to ROb (RTN-peri-ROb); 3) one probe correctly placed in ROb and one probe incorrectly placed in areas adjacent to RTN (peri-RTN-ROb); and 4) neither probe correctly placed (peri-RTN-peri-ROb). Focal simultaneous acidification of RTN-ROb significantly increased ventilation ((V) over dot E) up to 22% compared with baseline, with significant increases in both breathing frequency and tidal volume. Focal acidification of RTN-peri-ROb increased (V) over dot E significantly by up to 15% compared with baseline. Focal acidification of ROb and peri-RTN had no significant effect. The simultaneous acidification of regions just outside the RTN and ROb actually decreased (V) over dot E by up to 11%. These results support a modulatory role for the ROb with respect to central chemoreception at the RTN.

Survival following mechanical ventilation of recipients of bone marrow transplants and peripheral blood stem cell transplants

Survival of bone marrow transplant recipients requiting mechanical ventilation is poor but improving. This study reports a retrospective audit of all haematopoietic stem cell transplant (HSCT) recipients requiring mechanical ventilation at an Australian institution over a period spanning 11 years from 1988 to 1998. Recipients of autologous transplants are significantly less likely to require mechanical ventilation than recipients of allogeneic transplants. Of 50 patients requiring mechanical ventilation, 28% survived to discharge from the intensive care unit, 20% to 30 days post-ventilation, 18% to discharge from hospital and 12% to six months post-ventilation. Risk factors for mortality in the HSCT recipient requiting mechanical ventilation include renal, hepatic and cardiovascular insufficiency and greater severity of illness. Mechanical ventilation of HSCT recipients should not be regarded as futile therapy.

The development and psychometric properties of a measure of social and adaptive functioning for children and adolescents

Developed, piloted, and examined the psychometric properties of the Child and Adolescent Social and Adaptive Functioning Scale (CASAFS), a self-report measure designed to examine the social functioning of young people in the areas of school performance, peer relationships, family relationships, and home duties/self-care. The findings of confirmatory and exploratory factor analysis support a 4-factor solution consistent with the hypothesized domains. Fit indexes suggested that the 4-correlated factor model represented a satisfactory solution for the data, with the covariation between factors being satisfactorily explained by a single, higher order factor reflecting social and adaptive functioning in general. The internal consistency and 12-month test-retest reliability of the total scale was acceptable. A significant, negative correlation was found between the CASAFS and a measure of depressive symptoms, showing that high levels of social functioning are associated with low levels of depression. Significant differences in CASAFS total and subscale scores were found between clinically depressed adolescents and a matched sample of nonclinical controls. Adolescents who reported elevated but subclinical levels of depression also reported lower levels of social functioning in comparison to nonclinical controls.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Service offloading in adaptive real-time systems

Smartphones and other internet enabled devices are now common on our everyday life, thus unsurprisingly a current trend is to adapt desktop PC applications to execute on them. However, since most of these applications have quality of service (QoS) requirements, their execution on resource-constrained mobile devices presents several challenges. One solution to support more stringent applications is to offload some of the applications’ services to surrogate devices nearby. Therefore, in this paper, we propose an adaptable offloading mechanism which takes into account the QoS requirements of the application being executed (particularly its real-time requirements), whilst allowing offloading services to several surrogate nodes. We also present how the proposed computing model can be implemented in an Android environment

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

A Communication and Resources Management Scheme to Support the Smart Grid Integration of Multiplayers Access to Resources Information

The increasing and intensive integration of distributed energy resources into distribution systems requires adequate methodologies to ensure a secure operation according to the smart grid paradigm. In this context, SCADA (Supervisory Control and Data Acquisition) systems are an essential infrastructure. This paper presents a conceptual design of a communication and resources management scheme based on an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). The methodology is used to support the energy resource management considering all the involved costs, power flows, and electricity prices leading to the network reconfiguration. The methodology also addresses the definition of the information access permissions of each player to each resource. The paper includes a 33-bus network used in a case study that considers an intensive use of distributed energy resources in five distinct implemented operation contexts.

Adaptive offloading for infotainment systems

Infotainment applications in vehicles are currently supported both by the in-vehicle platform, as well as by user’s smart devices, such as smartphones and tablets. More and more the user expects that there is a continuous service of applications inside or outside of the vehicle, provided in any of these devices (a simple but common example is hands-free mobile phone calls provided by the vehicle platform). With the increasing complexity of ‘apps’, it is necessary to support increasing levels of Quality of Service (QoS), with varying resource requirements. Users may want to start listening to music in the smartphone, or video in the tablet, being this application transparently ‘moved’ into the vehicle when it is started. This paper presents an adaptable offloading mechanism, following a service-oriented architecture pattern, which takes into account the QoS requirements of the applications being executed when making decisions.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.