Stochastic growth models for analyzing crustacean data

The contemporary methodology for growth models of organisms is based on continuous trajectories and thus it hinders us from modelling stepwise growth in crustacean populations. Growth models for fish are normally assumed to follow a continuous function, but a different type of model is needed for crustacean growth. Crustaceans must moult in order for them to grow. The growth of crustaceans is a discontinuous process due to the periodical shedding of the exoskeleton in moulting. The stepwise growth of crustaceans through the moulting process makes the growth estimation more complex. Stochastic approaches can be used to model discontinuous growth or what are commonly known as "jumps" (Figure 1). However, in stochastic growth model we need to ensure that the stochastic growth model results in only positive jumps. In view of this, we will introduce a subordinator that is a special case of a Levy process. A subordinator is a non-decreasing Levy process, that will assist in modelling crustacean growth for better understanding of the individual variability and stochasticity in moulting periods and increments. We develop the estimation methods for parameter estimation and illustrate them with the help of a dataset from laboratory experiments. The motivational dataset is from the ornate rock lobster, Panulirus ornatus, which can be found between Australia and Papua New Guinea. Due to the presence of sex effects on the growth (Munday et al., 2004), we estimate the growth parameters separately for each sex. Since all hard parts are shed too often, the exact age determination of a lobster can be challenging. However, the growth parameters for the aforementioned moult processes from tank data being able to estimate through: (i) inter-moult periods, and (ii) moult increment. We will attempt to derive a joint density, which is made up of two functions: one for moult increments and the other for time intervals between moults. We claim these functions are conditionally independent given pre-moult length and the inter-moult periods. The variables moult increments and inter-moult periods are said to be independent because of the Markov property or conditional probability. Hence, the parameters in each function can be estimated separately. Subsequently, we integrate both of the functions through a Monte Carlo method. We can therefore obtain a population mean for crustacean growth (e. g. red curve in Figure 1). [GRAPHICS]

Load estimation with uncertainties from opportunistic sampling data: A semiparametric approach

We consider estimating the total load from frequent flow data but less frequent concentration data. There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates that minimizes the biases and makes use of informative predictive variables. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized rating-curve approach with additional predictors that capture unique features in the flow data, such as the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and the discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. Forming this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach for two rivers delivering to the Great Barrier Reef, Queensland, Australia. One is a data set from the Burdekin River, and consists of the total suspended sediment (TSS) and nitrogen oxide (NO(x)) and gauged flow for 1997. The other dataset is from the Tully River, for the period of July 2000 to June 2008. For NO(x) Burdekin, the new estimates are very similar to the ratio estimates even when there is no relationship between the concentration and the flow. However, for the Tully dataset, by incorporating the additional predictive variables namely the discounted flow and flow phases (rising or recessing), we substantially improved the model fit, and thus the certainty with which the load is estimated.

An extended regression approach to estimating loads and their uncertainties in Great Barrier Reef catchments

There are numerous load estimation methods available, some of which are captured in various online tools. However, most estimators are subject to large biases statistically, and their associated uncertainties are often not reported. This makes interpretation difficult and the estimation of trends or determination of optimal sampling regimes impossible to assess. In this paper, we first propose two indices for measuring the extent of sampling bias, and then provide steps for obtaining reliable load estimates by minimizing the biases and making use of possible predictive variables. The load estimation procedure can be summarized by the following four steps: - (i) output the flow rates at regular time intervals (e.g. 10 minutes) using a time series model that captures all the peak flows; - (ii) output the predicted flow rates as in (i) at the concentration sampling times, if the corresponding flow rates are not collected; - (iii) establish a predictive model for the concentration data, which incorporates all possible predictor variables and output the predicted concentrations at the regular time intervals as in (i), and; - (iv) obtain the sum of all the products of the predicted flow and the predicted concentration over the regular time intervals to represent an estimate of the load. The key step to this approach is in the development of an appropriate predictive model for concentration. This is achieved using a generalized regression (rating-curve) approach with additional predictors that capture unique features in the flow data, namely the concept of the first flush, the location of the event on the hydrograph (e.g. rise or fall) and cumulative discounted flow. The latter may be thought of as a measure of constituent exhaustion occurring during flood events. The model also has the capacity to accommodate autocorrelation in model errors which are the result of intensive sampling during floods. Incorporating this additional information can significantly improve the predictability of concentration, and ultimately the precision with which the pollutant load is estimated. We also provide a measure of the standard error of the load estimate which incorporates model, spatial and/or temporal errors. This method also has the capacity to incorporate measurement error incurred through the sampling of flow. We illustrate this approach using the concentrations of total suspended sediment (TSS) and nitrogen oxide (NOx) and gauged flow data from the Burdekin River, a catchment delivering to the Great Barrier Reef. The sampling biases for NOx concentrations range from 2 to 10 times indicating severe biases. As we expect, the traditional average and extrapolation methods produce much higher estimates than those when bias in sampling is taken into account.

A maximum likelihood approach for estimating growth from tag–recapture data

The Fabens method is commonly used to estimate growth parameters k and l infinity in the von Bertalanffy model from tag-recapture data. However, the Fabens method of estimation has an inherent bias when individual growth is variable. This paper presents an asymptotically unbiassed method using a maximum likelihood approach that takes account of individual variability in both maximum length and age-at-tagging. It is assumed that each individual's growth follows a von Bertalanffy curve with its own maximum length and age-at-tagging. The parameter k is assumed to be a constant to ensure that the mean growth follows a von Bertalanffy curve and to avoid overparameterization. Our method also makes more efficient use nf thp measurements at tno and recapture and includes diagnostic techniques for checking distributional assumptions. The method is reasonably robust and performs better than the Fabens method when individual growth differs from the von Bertalanffy relationship. When measurement error is negligible, the estimation involves maximizing the profile likelihood of one parameter only. The method is applied to tag-recapture data for the grooved tiger prawn (Penaeus semisulcatus) from the Gulf of Carpentaria, Australia.

The bentiromide test using plasma p-aminobenzoic acid for diagnosing pancreatic insufficiency in young children. The effect of two different doses and a liquid meal

The bentiromide test was evaluated using plasma p-aminobenzoic acid as an indirect test of pancreatic insufficiency in young children between 2 months and 4 years of age. To determine the optimal test method, the following were examined: (a) the best dose of bentiromide (15 mg/kg or 30 mg/kg); (b) the optimal sampling time for plasma p-aminobenzoic acid, and; (c) the effect of coadministration of a liquid meal. Sixty-nine children (1.6 ± 1.0 years) were studied, including 34 controls with normal fat absorption and 35 patients (34 with cystic fibrosis) with fat maldigestion due to pancreatic insufficiency. Control and pancreatic insufficient subjects were studied in three age-matched groups: (a) low-dose bentiromide (15 mg/kg) with clear fluids; (b) high-dose bentiromide (30 mg/kg) with clear fluids, and; (c) high-dose bentiromide with a liquid meal. Plasma p-aminobenzoic acid was determined at 0, 30, 60, and 90 minutes then hourly for 6 hours. The dose effect of bentiromide with clear liquids was evaluated. High-dose bentiromide best discriminated control and pancreatic insufficient subjects, due to a higher peak plasma p-aminobenzoic acid level in controls, but poor sensitivity and specificity remained. High-dose bentiromide with a liquid meal produced a delayed increase in plasma p-aminobenzoic acid in the control subjects probably caused by retarded gastric emptying. However, in the pancreatic insufficient subjects, use of a liquid meal resulted in significantly lower plasma p-aminobenzoic acid levels at all time points; plasma p-aminobenzoic acid at 2 and 3 hours completely discriminated between control and pancreatic insufficient patients. Evaluation of the data by area under the time-concentration curve failed to improve test results. In conclusion, the bentiromide test is a simple, clinically useful means of detecting pancreatic insufficiency in young children, but a higher dose administered with a liquid meal is recommended.

Estimating equations for parameters in stochastic growth models from tag-recapture data

James (1991, Biometrics 47, 1519-1530) constructed unbiased estimating functions for estimating the two parameters in the von Bertalanffy growth curve from tag-recapture data. This paper provides unbiased estimating functions for a class of growth models that incorporate stochastic components and explanatory variables. a simulation study using seasonal growth models indicates that the proposed method works well while the least-squares methods that are commonly used in the literature may produce substantially biased estimates. The proposed model and method are also applied to real data from tagged rack lobsters to assess the possible seasonal effect on growth.

An improved Fabens method for estimation of growth parameters in the vonBertalanffy model with individual asymptotes

In the analysis of tagging data, it has been found that the least-squares method, based on the increment function known as the Fabens method, produces biased estimates because individual variability in growth is not allowed for. This paper modifies the Fabens method to account for individual variability in the length asymptote. Significance tests using t-statistics or log-likelihood ratio statistics may be applied to show the level of individual variability. Simulation results indicate that the modified method reduces the biases in the estimates to negligible proportions. Tagging data from tiger prawns (Penaeus esculentus and Penaeus semisulcatus) and rock lobster (Panulirus ornatus) are analysed as an illustration.

Quantifying progressive anterior overgrowth in the thoracic vertebrae of adolescent idiopathic scoliosis patients

Study design Anterior and posterior vertebral body heights were measured from sequential MRI scans of adolescent idiopathic scoliosis (AIS) patients and healthy controls. Objective To measure changes in vertebral body height over time during scoliosis progression to assess how vertebral body height discrepancies change during growth. Summary of background data Relative anterior overgrowth has been proposed as a potential driver for AIS initiation and progression. This theory proposes that the anterior column grows faster, and the posterior column slower, in AIS patients when compared to healthy controls. There is disagreement in the literature as to whether the anterior vertebral body heights are proportionally greater than posterior vertebral body heights in AIS patients when compared to healthy controls. To some extent, these discrepancies may be attributed to methodological differences. Methods MRI scans of the major curve of 21 AIS patients (mean age 12.5 ± 1.4 years, mean Cobb 32.2 ± 12.8º) and between T4 and T12 of 21 healthy adolescents (mean age 12.1 ± 0.5 years) were captured for this study. Of the 21 AIS patients, 14 had a second scan on average 10.8 ± 4.7 months after the first. Anterior and posterior vertebral body heights were measured from the true sagittal plane of each vertebra such that anterior overgrowth could be quantified. Results The difference between anterior and posterior vertebral body height in healthy, non-scoliotic children was significantly greater than in AIS patients with mild to moderate scoliosis. However there was no significant relationship between the overall anterior-posterior vertebral body height difference in AIS and either severity of the curve or its progression over time. Conclusions Whilst AIS patients have a proportionally longer anterior column than non-scoliotic controls, the degree of anterior overgrowth was not related to the rate of progression or the severity of the scoliotic curve.

Parameterizing Unconditional Skewness in Models for Financial Time Series

In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate unconditional skewness. We consider modeling the unconditional mean and variance using models that respond nonlinearly or asymmetrically to shocks. We investigate the implications of these models on the third-moment structure of the marginal distribution as well as conditions under which the unconditional distribution exhibits skewness and nonzero third-order autocovariance structure. In this respect, an asymmetric or nonlinear specification of the conditional mean is found to be of greater importance than the properties of the conditional variance. Several examples are discussed and, whenever possible, explicit analytical expressions provided for all third-order moments and cross-moments. Finally, we introduce a new tool, the shock impact curve, for investigating the impact of shocks on the conditional mean squared error of return series.

Understanding how axial loads on the spine influence segmental biomechanics for idiopathic scoliosis patients: A magnetic resonance imaging study

Background Segmental biomechanics of the scoliotic spine are important since the overall spinal deformity is comprised of the cumulative coronal and axial rotations of individual joints. This study investigates the coronal plane segmental biomechanics for adolescent idiopathic scoliosis patients in response to physiologically relevant axial compression. Methods Individual spinal joint compliance in the coronal plane was measured for a series of 15 idiopathic scoliosis patients using axially loaded magnetic resonance imaging. Each patient was first imaged in the supine position with no axial load, and then again following application of an axial compressive load. Coronal plane disc wedge angles in the unloaded and loaded configurations were measured. Joint moments exerted by the axial compressive load were used to derive estimates of individual joint compliance. Findings The mean standing major Cobb angle for this patient series was 46°. Mean intra-observer measurement error for endplate inclination was 1.6°. Following loading, initially highly wedged discs demonstrated a smaller change in wedge angle, than less wedged discs for certain spinal levels (+ 2,+1,− 2 relative to the apex, (p < 0.05)). Highly wedged discs were observed near the apex of the curve, which corresponded to lower joint compliance in the apical region. Interpretation While individual patients exhibit substantial variability in disc wedge angles and joint compliance, overall there is a pattern of increased disc wedging near the curve apex, and reduced joint compliance in this region. Approaches such as this can provide valuable biomechanical data on in vivo spinal biomechanics of the scoliotic spine, for analysis of deformity progression and surgical planning.

Evolution in design of integral leg prosthesis leads to improved outcomes

Developments of surgical attachments for bone-anchored prostheses are slowly but surely winning over the initial disbelief in the orthopedic community. Clearly, this option is becoming accessible to a wide range of individuals with limb loss. Seminal studies have demonstrated that the pioneering procedure relying on screw-type fixation engenders major clinical benefits and acceptable safety. The surgical procedure for press-fit implants, such as the Integral-Leg-Prosthesis (ILP) has been described Dr Aschoff and his team. Some clinical benefits of press-fit implants have been also established. Here, his team is once again taking a leading role by sharing the progression over 15 years of the rate of deep infections for 69 individuals with transfemoral amputation fitted with three successive refined versions of the ILP. By definition, a double-blind randomized clinical trial to test the effect of different fixation’s design is difficult. Alternatively, Juhnke and colleagues are reporting the outcomes of action-research study for a cohort of participants. The first and foremost important outcome of this study is the confirmation that the current design of the IPL and rehabilitation program are altogether leading to an acceptable rate of deep infection and other adverse events (e.g., structural failure of implant, periprosthetic factures). This study is also providing a strong insight onto the effect of major phases in redesign of an implant on the risk of infection. This is an important reminder that the development of a successful osseointegrated implant is unlikely to be immediate but the results of a learning curve made of empirical and sequential changes led by a reflective clinical practice. Clearly, this study provided better understanding of the safety of the ILP surgical and rehabilitation procedure while establishing standards and benchmark data for future studies focusing on design and infection of press-fit implants. Complementary observations of relationship between infection and cofounders such as loading of the prosthesis and prosthetic components used would be beneficial.Further definitive evidences of the clinical benefits with the latest design would be valuable, although an increase in health related quality of life and functional outcomes are likely to be confirmed. Altogether, the authors are providing compelling evidence that bone-anchored attachments particularly those relying on press-fit implants are an established alternative to socket prostheses.

Nonlinear analysis for the pre- and post-yield behaviour of a composite structure with the refined plastic hinge approach

The computational technique of the full ranges of the second-order inelastic behaviour evaluation of steel-concrete composite structure is not always sought forgivingly, and therefore it hinders the development and application of the performance-based design approach for the composite structure. To this end, this paper addresses of the advanced computational technique of the higher-order element with the refined plastic hinges to capture the all-ranges behaviour of an entire steel-concrete composite structure. Moreover, this paper presents the efficient and economical cross-section analysis to evaluate the element section capacity of the non-uniform and arbitrary composite section subjected to the axial and bending interaction. Based on the same single algorithm, it can accurately and effectively evaluate nearly continuous interaction capacity curve from decompression to pure bending technically, which is the important capacity range but highly nonlinear. Hence, this cross-section analysis provides the simple but unique algorithm for the design approach. In summary, the present nonlinear computational technique can simulate both material and geometric nonlinearities of the composite structure in the accurate, efficient and reliable fashion, including partial shear connection and gradual yielding at pre-yield stage, plasticity and strain-hardening effect due to axial and bending interaction at post-yield stage, loading redistribution, second-order P-δ and P-Δ effect, and also the stiffness and strength deterioration. And because of its reliable and accurate behavioural evaluation, the present technique can be extended for the design of the high-strength composite structure and potentially for the fibre-reinforced concrete structure.

Feeding mode of Australian infants in the first 12 months of life: An assessment against national breastfeeding indicators

Background: In 2011, Australia published a set of 6 population-level indicators assessing breastfeeding, formula use, and the introduction of soft/semisolid/solid foods. Objectives: This study aimed to report the feeding practices of Australian infants against these indicators and determine the predictors of early breastfeeding cessation and introduction of solids. Methods: Mother–infant dyads (N = 1470) were recruited postnatally in 2 Australian capital cities and regional areas of 1 state between February 2008 and March 2009. Demographic and feeding intention data were collected by self-completed questionnaire at infant birth, with feeding practices (current feeding mode, age of breastfeeding cessation, age of formula and/or solids introduction) reported when the infant was between 4 and 7 months of age, and around 13 months of age. Multiple logistic regression was used to determine the predictors of breastfeeding cessation and solids introduction. Results: Although initiation of breastfeeding was almost universal (93.3%), less than half of the infants were breastfed to 6 months (41.7%) and 33.3% were receiving solids by 4 months. Women who were socially disadvantaged, younger, less educated, unpartnered, primiparous, and/or overweight were most likely to have ceased breastfeeding before 6 months of age, and younger and/or less educated women were most likely to have introduced solid food by 4 months of age. Not producing adequate milk was the most common reason provided for cessation of breastfeeding. Conclusion: The feeding behaviors of Australian infants in the first 12 months fall well short of recommendations. Women need anticipatory guidance as to the indicators of breastfeeding success and the tendency of women to doubt the adequacy of their breast milk supply warrants further investigation.

Corneal confocal microscopy predicts 4-year incident peripheral neuropathy in Type 1 diabetes

OBJECTIVE This study determined if deficits in corneal nerve fiber length (CNFL) assessed using corneal confocal microscopy (CCM) can predict future onset of diabetic peripheral neuropathy (DPN). RESEARCH DESIGN AND METHODS CNFL and a range of other baseline measures were compared between 90 nonneuropathic patients with type 1 diabetes who did or did not develop DPN after 4 years. The receiver operator characteristic (ROC) curve was used to determine the capability of single and combined measures of neuropathy to predict DPN. RESULTS DPN developed in 16 participants (18%) after 4 years. Factors predictive of 4-year incident DPN were lower CNFL (P = 0.041); longer duration of diabetes (P = 0.002); higher triglycerides (P = 0.023); retinopathy (higher on the Early Treatment of Diabetic Retinopathy Study scale) (P = 0.008); nephropathy (higher albumin-to-creatinine ratio) (P = 0.001); higher neuropathy disability score (P = 0.037); lower cold sensation (P = 0.001) and cold pain (P = 0.027) thresholds; higher warm sensation (P = 0.008), warm pain (P = 0.024), and vibration (P = 0.003) thresholds; impaired monofilament response (P = 0.003); and slower peroneal (P = 0.013) and sural (P = 0.002) nerve conduction velocity. CCM could predict the 4-year incident DPN with 63% sensitivity and 74% specificity for a CNFL threshold cutoff of 14.1 mm/mm2 (area under ROC curve = 0.66, P = 0.041). Combining neuropathy measures did not improve predictive capability. CONCLUSIONS DPN can be predicted by various demographic, metabolic, and conventional neuropathy measures. The ability of CCM to predict DPN broadens the already impressive diagnostic capabilities of this novel ophthalmic marker.

A novel approach to 6-DOF adaptive trajectory tracking control of an AUV in the presence of parameter uncertainties

In this paper, the trajectory tracking control of an autonomous underwater vehicle (AUVs) in six-degrees-of-freedom (6-DOFs) is addressed. It is assumed that the system parameters are unknown and the vehicle is underactuated. An adaptive controller is proposed, based on Lyapunov׳s direct method and the back-stepping technique, which interestingly guarantees robustness against parameter uncertainties. The desired trajectory can be any sufficiently smooth bounded curve parameterized by time even if consist of straight line. In contrast with the majority of research in this field, the likelihood of actuators׳ saturation is considered and another adaptive controller is designed to overcome this problem, in which control signals are bounded using saturation functions. The nonlinear adaptive control scheme yields asymptotic convergence of the vehicle to the reference trajectory, in the presence of parametric uncertainties. The stability of the presented control laws is proved in the sense of Lyapunov theory and Barbalat׳s lemma. Efficiency of presented controller using saturation functions is verified through comparing numerical simulations of both controllers.