Integration Process for the Habitat Demonstration Unit

The Habitat Demonstration Unit (HDU) is an experimental exploration habitat technology and architecture test platform designed for analog demonstration activities. The HDU previously served as a test bed for testing technologies and sub-systems in a terrestrial surface environment. in 2010 in the Pressurized Excursion Module (PEM) configuration. Due to the amount of work involved to make the HDU project successful, the HDU project has required a team to integrate a variety of contributions from NASA centers and outside collaborators The size of the team and number of systems involved With the HDU makes Integration a complicated process. However, because the HDU shell manufacturing is complete, the team has a head start on FY--11 integration activities and can focus on integrating upgrades to existing systems as well as integrating new additions. To complete the development of the FY-11 HDU from conception to rollout for operations in July 2011, a cohesive integration strategy has been developed to integrate the various systems of HDU and the payloads. The highlighted HDU work for FY-11 will focus on performing upgrades to the PEM configuration, adding the X-Hab as a second level, adding a new porch providing the astronauts a larger work area outside the HDU for EVA preparations, and adding a Hygiene module. Together these upgrades result in a prototype configuration of the Deep Space Habitat (DSH), an element under evaluation by NASA's Human Exploration Framework Team (HEFT) Scheduled activates include early fit-checks and the utilization of a Habitat avionics test bed prior to installation into HDU. A coordinated effort to utilize modeling and simulation systems has aided in design and integration concept development. Modeling tools have been effective in hardware systems layout, cable routing, sub-system interface length estimation and human factors analysis. Decision processes on integration and use of all new subsystems will be defined early in the project to maximize the efficiency of both integration and field operations. In addition a series of tailored design reviews are utilized to quickly define the systems and their integration into the DSH configuration. These processes are necessary to ensure activities, such as partially reversing integration of the X-Hab second story of the HDU and deploying and stowing the new work porch for transportation to the JSC Rock Yard and to the Arizona Black Point Lava Flow Site are performed with minimal or no complications. In addition, incremental test operations leading up to an Integrated systems test allows for an orderly systems test program. For FY-11 activities, the HDU DSH will act as a laboratory utilizing a new X-Hab inflatable second floor with crew habitation features. In addition to the day to day operations involving maintenance of the HDU and exploring the surrounding terrain, testing and optimizing the use of the new X-Hab, work porch, Hygiene Module, and other sub-system enhancements will be the focus of the FY-11 test objectives. The HDU team requires a successful integration strategy using a variety of tools and approaches to prepare the DSH for these test objectives. In a challenging environment where the prototyping influences the system design, as well as Vice versa, results of the HDU DSH field tests will influence future designs of habitat systems

Use of the bootstrap in analysing cost data from cluster randomised trials: some simulation results

BACKGROUND: This work has investigated under what conditions confidence intervals around the differences in mean costs from a cluster RCT are suitable for estimation using a commonly used cluster-adjusted bootstrap in preference to methods that utilise the Huber-White robust estimator of variance. The bootstrap's main advantage is in dealing with skewed data, which often characterise patient costs. However, it is insufficiently well recognised that one method of adjusting the bootstrap to deal with clustered data is only valid in large samples. In particular, the requirement that the number of clusters randomised should be large would not be satisfied in many cluster RCTs performed to date. METHODS: The performances of confidence intervals for simple differences in mean costs utilising a robust (cluster-adjusted) standard error and from two cluster-adjusted bootstrap procedures were compared in terms of confidence interval coverage in a large number of simulations. Parameters varied included the intracluster correlation coefficient, the sample size and the distributions used to generate the data. RESULTS: The bootstrap's advantage in dealing with skewed data was found to be outweighed by its poor confidence interval coverage when the number of clusters was at the level frequently found in cluster RCTs in practice. Simulations showed that confidence intervals based on robust methods of standard error estimation achieved coverage rates between 93.5% and 94.8% for a 95% nominal level whereas those for the bootstrap ranged between 86.4% and 93.8%. CONCLUSION: In general, 24 clusters per treatment arm is probably the minimum number for which one would even begin to consider the bootstrap in preference to traditional robust methods, for the parameter combinations investigated here. At least this number of clusters and extremely skewed data would be necessary for the bootstrap to be considered in favour of the robust method. There is a need for further investigation of more complex bootstrap procedures if economic data from cluster RCTs are to be analysed appropriately

Estimating preferences for a dermatology consultation using Best-Worst Scaling: Comparison of various methods of analysis

Background: Additional insights into patient preferences can be gained by supplementing discrete choice experiments with best-worst choice tasks. However, there are no empirical studies illustrating the relative advantages of the various methods of analysis within a random utility framework. Methods: Multinomial and weighted least squares regression models were estimated for a discrete choice experiment. The discrete choice experiment incorporated a best-worst study and was conducted in a UK NHS dermatology context. Waiting time, expertise of doctor, convenience of attending and perceived thoroughness of care were varied across 16 hypothetical appointments. Sample level preferences were estimated for all models and differences between patient subgroups were investigated using ovariateadjusted multinomial logistic regression. Results: A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it). Adjusting for covariates showed that patients who felt particularly affected by their skin condition during the previous week displayed extreme preference for short/no waiting time and were less concerned about other aspects of the appointment. Higher levels of educational attainment were associated with larger differences in utility between the levels of all attributes, although the attributes per use had the same impact upon choices as those with lower levels of attainment. The study also demonstrated the high levels of agreement between summary analyses using weighted least squares and estimates from multinomial models. Conclusion: Robust policy-relevant information on preferences can be obtained from discrete choice experiments incorporating best-worst questions with relatively small sample sizes. The separation of the effects due to attribute impact from the position of levels on the latent utility scale is not possible using traditional discrete choice experiments. This separation is important because health policies to change the levels of attributes in health care may be very different from those aiming to change the attribute impact per se. The good approximation of summary analyses to the multinomial model is a useful finding, because weighted least squares choice totals give better insights into the choice model and promote greater familiarity with the preference data

Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale

Background: Researchers are increasingly investigating the potential for ordinal tasks such as ranking and discrete choice experiments to estimate QALY health state values. However, the assumptions of random utility theory, which underpin the statistical models used to provide these estimates, have received insufficient attention. In particular, the assumptions made about the decisions between living states and the death state are not satisfied, at least for some people. Estimated values are likely to be incorrectly anchored with respect to death (zero) in such circumstances. Methods: Data from the Investigating Choice Experiments for the preferences of older people CAPability instrument (ICECAP) valuation exercise were analysed. The values (previously anchored to the worst possible state) were rescaled using an ordinal model proposed previously to estimate QALY-like values. Bootstrapping was conducted to vary artificially the proportion of people who conformed to the conventional random utility model underpinning the analyses. Results: Only 26% of respondents conformed unequivocally to the assumptions of conventional random utility theory. At least 14% of respondents unequivocally violated the assumptions. Varying the relative proportions of conforming respondents in sensitivity analyses led to large changes in the estimated QALY values, particularly for lower-valued states. As a result these values could be either positive (considered to be better than death) or negative (considered to be worse than death). Conclusion: Use of a statistical model such as conditional (multinomial) regression to anchor quality of life values from ordinal data to death is inappropriate in the presence of respondents who do not conform to the assumptions of conventional random utility theory. This is clearest when estimating values for that group of respondents observed in valuation samples who refuse to consider any living state to be worse than death: in such circumstances the model cannot be estimated. Only a valuation task requiring respondents to make choices in which both length and quality of life vary can produce estimates that properly reflect the preferences of all respondents

Risk Factors for Obesity: Further Evidence for Stronger Effects on Overweight Children and Adolescents Compared to Normal-Weight Subjects

Background: We recently showed that in preschoolers risk factors for overweight show stronger associations with BMI in children with high BMI values. However, it is unclear whether these findings might also pertain to adolescents. Methods: We extracted data on 3–10 year-old (n = 7,237) and 11–17 year-old (n = 5,986) children from a representative cross-sectional German health survey (KiGGS) conducted between 2003 and 2006 and calculated quantile regression models for each age group. We used z-scores of children's body mass index (BMI) as outcome variable and maternal BMI, maternal smoking in pregnancy, low parental socioeconomic status, exclusive formula-feeding and high TV viewing time as explanatory variables. Results: In both age groups, the estimated effects of all risk factors except formula-feeding on BMI z-score were greatest for children with the highest BMI z-score. The median BMI z-score of 11–17 year-old children with high TV viewing time, for example, was 0.11 [95% CI: 0.03, 0.19] units higher than the median BMI z-score of teenage children with low TV viewing time. This risk factor was associated with an average difference of 0.18 [0.06, 0.30] units at the 90th percentile of BMI z-score and of 0.20 [0.07, 0.33] units at the 97th percentile. Conclusions: We confirmed that risk factors for childhood overweight are associated with greater shifts in the upper parts of the children's BMI distribution than in the middle and lower parts. These findings pertain also to teenagers and might possibly help to explain the secular shift in the upper BMI percentiles in children and adolescents

Surgical treatment for acromioclavicular joint osteoarthritis: patient selection, surgical options, complications, and outcome

Osteoarthritis is one of the most common causes of pain originating from the acromioclavicular (AC) joint. An awareness of appropriate diagnostic techniques is necessary in order to localize clinical symptoms to the AC joint. Initial treatments for AC joint osteoarthritis, which include non-steroidal anti-inflammatory drugs (NSAIDS) and corticosteroids, are recommended prior to surgical interventions. Distal clavicle excision, the main surgical treatment option, can be performed by various surgical approaches, such as open procedures, direct arthroscopic, and indirect arthroscopic techniques. When choosing the best surgical option, factors such as avoidance of AC ligament damage, clavicular instability, and post-operative pain must be considered. This article examines patient selection, complications, and outcomes of surgical treatment options for AC joint osteoarthritis

Pervasiveness of Parasites in Pollinators

Many pollinator populations are declining, with large economic and ecological implications. Parasites are known to be an important factor in the some of the population declines of honey bees and bumblebees, but little is known about the parasites afflicting most other pollinators, or the extent of interspecific transmission or vectoring of parasites. Here we carry out a preliminary screening of pollinators (honey bees, five species of bumblebee, three species of wasp, four species of hoverfly and three genera of other bees) in the UK for parasites. We used molecular methods to screen for six honey bee viruses, Ascosphaera fungi, Microsporidia, and Wolbachia intracellular bacteria. We aimed simply to detect the presence of the parasites, encompassing vectoring as well as actual infections. Many pollinators of all types were positive for Ascosphaera fungi, while Microsporidia were rarer, being most frequently found in bumblebees. We also detected that most pollinators were positive for Wolbachia, most probably indicating infection with this intracellular symbiont, and raising the possibility that it may be an important factor in influencing host sex ratios or fitness in a diversity of pollinators. Importantly, we found that about a third of bumblebees (Bombus pascuorum and Bombus terrestris) and a third of wasps (Vespula vulgaris), as well as all honey bees, were positive for deformed wing virus, but that this virus was not present in other pollinators. Deformed wing virus therefore does not appear to be a general parasite of pollinators, but does interact significantly with at least three species of bumblebee and wasp. Further work is needed to establish the identity of some of the parasites, their spatiotemporal variation, and whether they are infecting the various pollinator species or being vectored. However, these results provide a first insight into the diversity, and potential exchange, of parasites in pollinator communities

Why Pleiotropic Interventions are Needed for Alzheimer's Disease

Alzheimer's disease (AD) involves a complex pathological cascade thought to be initially triggered by the accumulation of β-amyloid (Aβ) peptide aggregates or aberrant amyloid precursor protein (APP) processing. Much is known of the factors initiating the disease process decades prior to the onset of cognitive deficits, but an unclear understanding of events immediately preceding and precipitating cognitive decline is a major factor limiting the rapid development of adequate prevention and treatment strategies. Multiple pathways are known to contribute to cognitive deficits by disruption of neuronal signal transduction pathways involved in memory. These pathways are altered by aberrant signaling, inflammation, oxidative damage, tau pathology, neuron loss, and synapse loss. We need to develop stage-specific interventions that not only block causal events in pathogenesis (aberrant tau phosphorylation, Aβ production and accumulation, and oxidative damage), but also address damage from these pathways that will not be reversed by targeting prodromal pathways. This approach would not only focus on blocking early events in pathogenesis, but also adequately correct for loss of synapses, substrates for neuroprotective pathways (e.g., docosahexaenoic acid), defects in energy metabolism, and adverse consequences of inappropriate compensatory responses (aberrant sprouting). Monotherapy targeting early single steps in this complicated cascade may explain disappointments in trials with agents inhibiting production, clearance, or aggregation of the initiating Aβ peptide or its aggregates. Both plaque and tangle pathogenesis have already reached AD levels in the more vulnerable brain regions during the “prodromal” period prior to conversion to “mild cognitive impairment (MCI).” Furthermore, many of the pathological events are no longer proceeding in series, but are going on in parallel. By the MCI stage, we stand a greater chance of success by considering pleiotropic drugs or cocktails that can independently limit the parallel steps of the AD cascade at all stages, but that do not completely inhibit the constitutive normal functions of these pathways. Based on this hypothesis, efforts in our laboratories have focused on the pleiotropic activities of omega-3 fatty acids and the anti-inflammatory, antioxidant, and anti-amyloid activity of curcumin in multiple models that cover many steps of the AD pathogenic cascade (Cole and Frautschy, Alzheimers Dement 2:284–286, 2006)

Using Incentives and Social Information to Promote Energy Conservation Behavior

Improving the efficiency in the domestic energy consumption has become a showpiece of how behavioral economics can be applied to the field of environmental economics. This study builds upon the literature by providing subjects with individual and social energy performance information at group level in a controlled field experiment setting. We aim to test whether extrinsic incentives accentuate or crowd out the intrinsic motivation to save energy and how heterogeneity in environmental attitudes also impacts on electricity conservation. Besides, we test for the persistence of energy-saving habits after the information is removed. Results suggest that the provision of individual feedback and social information increase energy conserving behavior, with this being most effective among those who signaled in a previous stage preferences for pro-environmental and sustainable living. However, treatment variations indicate that subjects overall fail to maintain “good habits” once the intervention stops, with exception of pro-environmental subjects who continue to consume less electricity in the post-intervention phase. Furthermore, our findings indicate that rewarding groups in a competitive environment may create perverse long-run effects. While providing individual and social information could improve both consumer welfare and energy demand forecasting, the timescale, frequency, and mechanism undertaken require careful scrutiny and planning if these potential benefits are to be maximized and undesirable side effects prevented