Adjusting body cell mass for size in women of differing nutritional status

Background: Body cell mass (BCM) may be estimated in clinical practice to assess functional nutritional status, eg, in patients with anorexia nervosa. Interpretation of the data, especially in younger patients who are still growing, requires appropriate adjustment for size. Previous investigations of this general issue have addressed chemical rather than functional components of body composition and have not considered patients at the extremes of nutritional status, in whom the ability to make longitudinal comparisons is of particular importance. Objective: Our objective was to determine the power by which height should be raised to adjust BCM for height in women of differing nutritional status. Design: BCM was estimated by K-40 counting in 58 healthy women, 33 healthy female adolescents, and 75 female adolescents with anorexia nervosa. The relation between BCM and height was explored in each group by using log-log regression analysis. Results: The powers by which height should be raised to adjust BCM,A,ere 1.73. 1.73, and 2.07 in the women, healthy female adolescents, and anorexic female adolescents, respectively. A simplified version of the index, BCM/height(2), was appropriate for all 3 categories and was negligibly correlated with height. Conclusions: In normal-weight women, the relation between height and BCM is consistent with that reported previously between height and fat-free mass. Although the consistency of the relation between BCM and fat-free mass decreases with increasing weight loss, the relation between height and BCM is not significantly different between normal-weight and underweight women. The index BCM/height(2) is easy to calculate and applicable to both healthy and underweight women. This information may be helpful in interpreting body-composition data in clinical practice.

Disaster management in Nepal: Media engagement in the Post-2015 Framework for Disaster Risk Reduction

Nepal, as a consequence of its geographical location and changing climate, faces frequent threats of natural disasters. According to the World Bank’s 2005 Natural Disasters Hotspots Report, Nepal is ranked the 11th most vulnerable country to earthquake and 30th to flood risk. Geo-Hazards International (2011) has classified Kathmandu as one of the world’s most vulnerable cities to earthquakes. In the last four decades more than 32,000 people in Nepal have lost their lives and annual monetary loss is estimated at more than 15 million (US) dollars. This review identifies gaps in knowledge, and progress towards implementation of the Post Hyogo Framework of Action. Nepal has identified priority areas: community resilience, sustainable development and climate change induced disaster risk reduction. However, one gap between policy and action lies in the ability of Nepal to act effectively in accordance with an appropriate framework for media activities. Supporting media agencies include the Press Council, Federation of Nepalese Journalists, Nepal Television, Radio Nepal and Telecommunications Authority and community based organizations. The challenge lies in further strengthening traditional and new media to undertake systematic work supported by government bodies and the National Risk Reduction Consortium (NRRC). Within this context, the ideal role for media is one that is proactive where journalists pay attention to a range of appropriate angles or frames when preparing and disseminating information. It is important to develop policy for effective information collection, sharing and dissemination in collaboration with Telecommunication, Media and Journalists. The aim of this paper is to describe the developments in disaster management in Nepal and their implications for media management. This study provides lessons for government, community and the media to help improve the framing of disaster messages. Significantly, the research highlights the prominence that should be given to flood, landslides, lightning and earthquakes.

Older adults and driving reduction: Is the gender gap narrowing?

This study investigated driving reduction in a diverse sample of 229 male and female older drivers aged 70 years and above in Queensland, Australia. The study sought to determine whether differences existed between male and female older drivers in regard to driving patterns, and to identify factors that were predictive of driving reduction in female versus male older drivers. Participants provided information on their health, self-reported driving patterns, driving perceptions, alternative transport options, and feedback. Overall, females were more likely to avoid challenging situations but less likely to reduce their driving when compared to males. Self-rated health and driving confidence were significant predictors for driving reduction among females. For males, driving importance was the only significant predictor for driving reduction in this sample. This study indicates the need for longitudinal research on the process of driving reduction and whether the planning process for driving cessation differ between females and males.

Reduction in arterial wall strain with aggressive lipid-lowering therapy in patients with carotid artery disease

Background: Inflammation and biomechanical factors have been associated with the development of vulnerable atherosclerotic plaques. Lipid-lowering therapy has been shown to be effective in stabilizing them by reducing plaque inflammation. Its effect on arterial wall strain, however, remains unknown. The aim of the present study was to investigate the role of high- and low-dose lipid-lowering therapy using an HMG-CoA reductase inhibitor, atorvastatin, on arterial wall strain. Methods and Results: Forty patients with carotid stenosis >40% were successfully followed up during the Atorvastatin Therapy: Effects on Reduction Of Macrophage Activity (ATHEROMA; ISRCTN64894118) Trial. All patients had plaque inflammation as shown by intraplaque accumulation of ultrasmall super paramagnetic particles of iron oxide on magnetic resonance imaging at baseline. Structural analysis was performed and change of strain was compared between high- and low-dose statin at 0 and 12 weeks. There was no significant difference in strain between the 2 groups at baseline (P=0.6). At 12 weeks, the maximum strain was significantly lower in the 80-mg group than in the 10-mg group (0.085±0.033 vs. 0.169±0.084; P=0.001). A significant reduction (26%) of maximum strain was observed in the 80-mg group at 12 weeks (0.018±0.02; P=0.01). Conclusions: Aggressive lipid-lowering therapy is associated with a significant reduction in arterial wall strain. The reduction in biomechanical strain may be associated with reductions in plaque inflammatory burden.

Image-based midsole insert design and the material effects on heel plantar pressure distribution during simulated walking loads

The primary objective of this paper is to study the use of medical image-based finite element (FE) modelling in subjectspecific midsole design and optimisation for heel pressure reduction using a midsole plug under the calcaneus area (UCA). Plugs with different relative dimensions to the size of the calcaneus of the subject have been incorporated in the heel region of the midsole. The FE foot model was validated by comparing the numerically predicted plantar pressure with biomechanical tests conducted on the same subject. For each UCA midsole plug design, the effect of material properties and plug thicknesses on the plantar pressure distribution and peak pressure level during the heel strike phase of normal walking was systematically studied. The results showed that the UCA midsole insert could effectively modify the pressure distribution, and its effect is directly associated with the ratio of the plug dimension to the size of the calcaneus bone of the subject. A medium hardness plug with a size of 95% of the calcaneus has achieved the best performance for relieving the peak pressure in comparison with the pressure level for a solid midsole without a plug, whereas a smaller plug with a size of 65% of the calcaneus insert with a very soft material showed minimum beneficial effect for the pressure relief.

The ATHEROMA (Atorvastatin Therapy: Effects on Reduction of Macrophage Activity) Study. Evaluation using ultrasmall superparamagnetic iron oxide-enhanced magnetic resonance imaging in carotid disease

Objectives: The aim of this study was to evaluate the effects of low-dose (10 mg) and high-dose (80 mg) atorvastatin on carotid plaque inflammation as determined by ultrasmall superparamagnetic iron oxide (USPIO)-enhanced carotid magnetic resonance imaging (MRI). The hypothesis was that treatment with 80 mg atorvastatin would demonstrate quantifiable changes in USPIO-enhanced MRI-defined inflammation within the first 3 months of therapy. Background: Preliminary studies indicate that USPIO-enhanced MRI can identify macrophage infiltration in human carotid atheroma in vivo and hence may be a surrogate marker of plaque inflammation. Methods: Forty-seven patients with carotid stenosis >40% on duplex ultrasonography and who demonstrated intraplaque accumulation of USPIO on MRI at baseline were randomly assigned in a balanced, double-blind manner to either 10 or 80 mg atorvastatin daily for 12 weeks. Baseline statin therapy was equivalent to 10 mg of atorvastatin or less. The primary end point was change from baseline in signal intensity (ΔSI) on USPIO-enhanced MRI in carotid plaque at 6 and 12 weeks. Results: Twenty patients completed 12 weeks of treatment in each group. A significant reduction from baseline in USPIO-defined inflammation was observed in the 80-mg group at both 6 weeks (ΔSI 0.13; p = 0.0003) and at 12 weeks (ΔSI 0.20; p < 0.0001). No difference was observed with the low-dose regimen. The 80-mg atorvastatin dose significantly reduced total cholesterol by 15% (p = 0.0003) and low-density lipoprotein cholesterol by 29% (p = 0.0001) at 12 weeks. Conclusions: Aggressive lipid-lowering therapy over a 3-month period is associated with significant reduction in USPIO-defined inflammation. USPIO-enhanced MRI methodology may be a useful imaging biomarker for the screening and assessment of therapeutic response to "anti-inflammatory" interventions in patients with atherosclerotic lesions. (Effects of Atorvastatin on Macrophage Activity and Plaque Inflammation Using Magnetic Resonance Imaging [ATHEROMA]; NCT00368589).

Atheroma: Is calcium important or not? A modelling study of stress within the atheromatous fibrous cap in relation to position and size of calcium deposits

Atheromatous plaque rupture h the cause of the majority of strokes and heart attacks in the developed world. The role of calcium deposits and their contribution to plaque vulnerability are controversial. Some studies have suggested that calcified plaque tends to be more stable whereas others have suggested the opposite. This study uses a finite element model to evaluate the effect of calcium deposits on the stress within the fibrous cap by varying their location and size. Plaque fibrous cap, lipid pool and calcification were modeled as hyperelastic, Isotropic, (nearly) incompressible materials with different properties for large deformation analysis by assigning time-dependent pressure loading on the lumen wall. The stress and strain contours were illustrated for each condition for comparison. Von Mises stress only increases up to 1.5% when varying the location of calcification in the lipid pool distant to the fibrous cap. Calcification in the fibrous cap leads to a 43% increase of Von Mises stress when compared with that in the lipid pool. An increase of 100% of calcification area leads to a 15% stress increase in the fibrous cap. Calcification in the lipid pool does not increase fibrous cap stress when it is distant to the fibrous cap, whilst large areas of calcification close to or in the fibrous cap may lead to a high stress concentration within the fibrous cap, which may cause plaque rupture. This study highlights the application of a computational model on a simulation of clinical problems, and it may provide insights into the mechanism of plaque rupture.

Improved estimation of size-transition matrices using tag-recapture data

We derive a new method for determining size-transition matrices (STMs) that eliminates probabilities of negative growth and accounts for individual variability. STMs are an important part of size-structured models, which are used in the stock assessment of aquatic species. The elements of STMs represent the probability of growth from one size class to another, given a time step. The growth increment over this time step can be modelled with a variety of methods, but when a population construct is assumed for the underlying growth model, the resulting STM may contain entries that predict negative growth. To solve this problem, we use a maximum likelihood method that incorporates individual variability in the asymptotic length, relative age at tagging, and measurement error to obtain von Bertalanffy growth model parameter estimates. The statistical moments for the future length given an individual's previous length measurement and time at liberty are then derived. We moment match the true conditional distributions with skewed-normal distributions and use these to accurately estimate the elements of the STMs. The method is investigated with simulated tag-recapture data and tag-recapture data gathered from the Australian eastern king prawn (Melicertus plebejus).

Statistical power calculation and sample size determination for environmental studies with data below detection limits

Power calculation and sample size determination are critical in designing environmental monitoring programs. The traditional approach based on comparing the mean values may become statistically inappropriate and even invalid when substantial proportions of the response values are below the detection limits or censored because strong distributional assumptions have to be made on the censored observations when implementing the traditional procedures. In this paper, we propose a quantile methodology that is robust to outliers and can also handle data with a substantial proportion of below-detection-limit observations without the need of imputing the censored values. As a demonstration, we applied the methods to a nutrient monitoring project, which is a part of the Perth Long-Term Ocean Outlet Monitoring Program. In this example, the sample size required by our quantile methodology is, in fact, smaller than that by the traditional t-test, illustrating the merit of our method.

An extravariation model for improving confidence intervals of population size estimates from removal data

We propose a new model for estimating the size of a population from successive catches taken during a removal experiment. The data from these experiments often have excessive variation, known as overdispersion, as compared with that predicted by the multinomial model. The new model allows catchability to vary randomly among samplings, which accounts for overdispersion. When the catchability is assumed to have a beta distribution, the likelihood function, which is refered to as beta-multinomial, is derived, and hence the maximum likelihood estimates can be evaluated. Simulations show that in the presence of extravariation in the data, the confidence intervals have been substantially underestimated in previous models (Leslie-DeLury, Moran) and that the new model provides more reliable confidence intervals. The performance of these methods was also demonstrated using two real data sets: one with overdispersion, from smallmouth bass (Micropterus dolomieu), and the other without overdispersion, from rat (Rattus rattus).

An optimal design for screening trials

Yao, Begg, and Livingston (1996, Biometrics 52, 992-1001) considered the optimal group size for testing a series of potentially therapeutic agents to identify a promising one as soon as possible for given error rates. The number of patients to be tested with each agent was fixed as the group size. We consider a sequential design that allows early acceptance and rejection, and we provide an optimal strategy to minimize the sample sizes (patients) required using Markov decision processes. The minimization is under the constraints of the two types (false positive and false negative) of error probabilities, with the Lagrangian multipliers corresponding to the cost parameters for the two types of errors. Numerical studies indicate that there can be a substantial reduction in the number of patients required.

Size-dependent natural mortality of juvenile banana prawns Penaeus merguiensis in the Gulf of Carpentaria, Australia

Natural mortality of marine invertebrates is often very high in the early life history stages and decreases in later stages. The possible size-dependent mortality of juvenile banana prawns, P. merguiensis (2-15 mm carapace length) in the Gulf of Carpentaria was investigated. The analysis was based on the data collected at 2-weekly intervals by beam trawls at four sites over a period of six years (between September 1986 and March 1992). It was assumed that mortality was a parametric function of size, rather than a constant. Another complication in estimating mortality for juvenile banana prawns is that a significant proportion of the population emigrates from the study area each year. This effect was accounted for by incorporating the size-frequency pattern of the emigrants in the analysis. Both the extra parameter in the model required to describe the size dependence of mortality, and that used to account for emigration were found to be significantly different from zero, and the instantaneous mortality rate declined from 0.89 week(-1) for 2 mm prawns to 0.02 week(-1) for 15 mm prawns.

Does the size of subsamples taken from multispecies trawl catches affect estimates of catch composition and abundance?

Although subsampling is a common method for describing the composition of large and diverse trawl catches, the accuracy of these techniques is often unknown. We determined the sampling errors generated from estimating the percentage of the total number of species recorded in catches, as well as the abundance of each species, at each increase in the proportion of the sorted catch. We completely partitioned twenty prawn trawl catches from tropical northern Australia into subsamples of about 10 kg each. All subsamples were then sorted, and species numbers recorded. Catch weights ranged from 71 to 445 kg, and the number of fish species in trawls ranged from 60 to 138, and invertebrate species from 18 to 63. Almost 70% of the species recorded in catches were "rare" in subsamples (less than one individual per 10 kg subsample or less than one in every 389 individuals). A matrix was used to show the increase in the total number of species that were recorded in each catch as the percentage of the sorted catch increased. Simulation modelling showed that sorting small subsamples (about 10% of catch weights) identified about 50% of the total number of species caught in a trawl. Larger subsamples (50% of catch weight on average) identified about 80% of the total species caught in a trawl. The accuracy of estimating the abundance of each species also increased with increasing subsample size. For the "rare" species, sampling error was around 80% after sorting 10% of catch weight and was just less than 50% after 40% of catch weight had been sorted. For the "abundant" species (five or more individuals per 10 kg subsample or five or more in every 389 individuals), sampling error was around 25% after sorting 10% of catch weight, but was reduced to around 10% after 40% of catch weight had been sorted.

A Bayesian decision approach for sample size determination in phase II trials

Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics 50, 337-349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion: of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one-stage design becomes twice as efficient as Stallard's one-stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.

Bias reduction using stochastic approximation

The paper studies stochastic approximation as a technique for bias reduction. The proposed method does not require approximating the bias explicitly, nor does it rely on having independent identically distributed (i.i.d.) data. The method always removes the leading bias term, under very mild conditions, as long as auxiliary samples from distributions with given parameters are available. Expectation and variance of the bias-corrected estimate are given. Examples in sequential clinical trials (non-i.i.d. case), curved exponential models (i.i.d. case) and length-biased sampling (where the estimates are inconsistent) are used to illustrate the applications of the proposed method and its small sample properties.