In situ investigation of explosive crystallization in a-Ge: Experimental determination of the interface response function using dynamic transmission electron microscopy

The crystallization of amorphous semiconductors is a strongly exothermic process. Once initiated the release of latent heat can be sufficient to drive a self-sustaining crystallization front through the material in a manner that has been described as explosive. Here, we perform a quantitative in situ study of explosive crystallization in amorphous germanium using dynamic transmission electron microscopy. Direct observations of the speed of the explosive crystallization front as it evolves along a laser-imprinted temperature gradient are used to experimentally determine the complete interface response function (i.e., the temperature-dependent front propagation speed) for this process, which reaches a peak of 16 m/s. Fitting to the Frenkel-Wilson kinetic law demonstrates that the diffusivity of the material locally/immediately in advance of the explosive crystallization front is inconsistent with those of a liquid phase. This result suggests a modification to the liquid-mediated mechanism commonly used to describe this process that replaces the phase change at the leading amorphous-liquid interface with a change in bonding character (from covalent to metallic) occurring in the hot amorphous material.

The post-illumination pupil response of melanopsin expressing retinal ganglion cells

Purpose The post-illumination pupil response (PIPR) has been quantified in the literature by four metrics. The spectral sensitivity of only one metric is known and this study quantifies the other three. To optimize the measurement of the PIPR in humans, we also determine the stimulus protocol producing the largest PIPR, the duration of the PIPR, and the metric(s) with the lowest coefficient of variation. Methods The consensual pupil light reflex (PLR) was measured with a Maxwellian view pupillometer (35.6° diameter stimulus). - Experiment 1: Spectral sensitivity of four PIPR metrics [plateau, 6 s, area under curve (AUC) early and late recovery] was determined from a criterion PIPR (n = 2 participants) to a 1 s pulse at five wavelengths (409-592nm) and fitted with Vitamin A nomogram (ƛmax = 482 nm). - Experiment 2: The PLR was measured in five healthy participants [29 to 42 years (mean = 32.6 years)] as a function of three stimulus durations (1 s, 10 s, 30 s), five irradiances spanning low to high melanopsin excitation levels (retinal irradiance: 9.8 to 14.8 log quanta.cm-2.s-1), and two wavelengths, one with high (465 nm) and one with low (637 nm) melanopsin excitation. Intra and inter-individual coefficients of variation (CV) were calculated. Results The melanopsin (opn4) photopigment nomogram adequately described the spectral sensitivity derived from all four PIPR metrics. The largest PIPR amplitude was observed with 1 s short wavelength pulses (retinal irradiance ≥ 12.8 log quanta.cm-2.s-1). Of the 4 PIPR metrics, the plateau and 6 s PIPR showed the least intra and inter-individual CV (≤ 0.2). The maximum duration of the sustained PIPR was 83.4 ± 48.0 s (mean ± SD) for 1 s pulses and 180.1 ± 106.2 s for 30 s pulses (465 nm; 14.8 log quanta.cm-2.s-1). Conclusions All current PIPR metrics provide a direct measure of intrinsic melanopsin retinal ganglion cell function. To measure progressive changes in melanopsin function in disease, we recommend that the intrinsic melanopsin response should be measured using a 1 s pulse with high melanopsin excitation and the PIPR should be analyzed with the plateau and/or 6 s metrics. That the PIPR can have a sustained constriction for as long as 3 minutes, our PIPR duration data provide a baseline for the selection of inter-stimulus intervals between consecutive pupil testing sequences.

The nature of malnutrition in children with end-stage liver disease awaiting orthotopic liver transplantation

To evaluate malnutrition in chronic liver disease, and its relationship to nutrient deficiencies and hepatic dysfunction. 27 children with end-stage liver disease were studied. Mean protein-energy intakes were 70% of recommended daily intakes. The patients were underweight and stunted with reduced mean triceps and subscapular skinfold thicknesses and midupper arm circumference. Mean total body potassium was only 63 ± 18% of that expected for age and sex. Deficiency of essential fatty acids (32%), and low concentrations of fat-soluble vitamins (A, 92%; E, 32%), iron (32%), zinc (42%), and selenium (13%) were common. Serum ammonia concentrations were raised in all patients, and increased methionine, tyrosine, and glutamic acid, and reduced glutamine concentrations were noted. There was no correlation between the degree of malnutrition and the degree of liver synthetic function, the degree of cholestasis, or the degree of liver injury. We suggest that potentially correctable factors in addition to liver failure (eg, inadequate absorbed intake) were important determinants of malnutrition in these patients.

A new method for the in vivo identification of mechanical properties in arteries from cine MRI images: Theoretical framework and validation

Quantifying the stiffness properties of soft tissues is essential for the diagnosis of many cardiovascular diseases such as atherosclerosis. In these pathologies it is widely agreed that the arterial wall stiffness is an indicator of vulnerability. The present paper focuses on the carotid artery and proposes a new inversion methodology for deriving the stiffness properties of the wall from cine-MRI (magnetic resonance imaging) data. We address this problem by setting-up a cost function defined as the distance between the modeled pixel signals and the measured ones. Minimizing this cost function yields the unknown stiffness properties of both the arterial wall and the surrounding tissues. The sensitivity of the identified properties to various sources of uncertainty is studied. Validation of the method is performed on a rubber phantom. The elastic modulus identified using the developed methodology lies within a mean error of 9.6%. It is then applied to two young healthy subjects as a proof of practical feasibility, with identified values of 625 kPa and 587 kPa for one of the carotid of each subject.

Temporal dependence of in vivo USPIO-enhanced MRI signal changes in human carotid atheromatous plaques

Introduction: Ultrasmall superparamagnetic iron oxide (USPIO)-enhanced MRI has been shown to be a useful modality to image activated macrophages in vivo, which are principally responsible for plaque inflammation. This study determined the optimum imaging time-window to detect maximal signal change post-USPIO infusion using T1-weighted (T1w), T2*- weighted (T2*w) and quantitative T2*(qT 2*) imaging. Methods: Six patients with an asymptomatic carotid stenosis underwent high resolution T1w, T2*w and qT2*MR imaging of their carotid arteries at 1.5 T. Imaging was performed before and at 24, 36, 48, 72 and 96 h after USPIO (Sinerem™, Guerbet, France) infusion. Each slice showing atherosclerotic plaque was manually segmented into quadrants and signal changes in each quadrant were fitted to an exponential power function to model the optimum time for post-infusion imaging. Results: The power function determining the mean time to convergence for all patients was 46, 41 and 39 h for the T1w, T 2*w and qT2*sequences, respectively. When modelling each patient individually, 90% of the maximum signal intensity change was observed at 36 h for three, four and six patients on T1w, T 2*w and qT2*, respectively. The rates of signal change decrease after this period but signal change was still evident up to 96 h. Conclusion: This study showed that a suitable imaging window for T 1w, T2*w and qT2*signal changes post-USPIO infusion was between 36 and 48 h. Logistically, this would be convenient in bringing patients back for one post-contrast MRI, but validation is required in a larger cohort of patients.

Model selection with misspecified spatial covariance structure

Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC)) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square ( [GRAPHICS] ), and overall RIC performs well.

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]

Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection

A modeling paradigm is proposed for covariate, variance and working correlation structure selection for longitudinal data analysis. Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection. This leads to a stepwise model selection procedure that deploys a combination of different model selection criteria. Although these criteria find a common theoretical root based on approximating the Kullback-Leibler distance, they are designed to address different aspects of model selection and have different merits and limitations. For example, the extended quasi-likelihood information criterion (EQIC) with a covariance penalty performs well for covariate selection even when the working variance function is misspecified, but EQIC contains little information on correlation structures. The proposed model selection strategies are outlined and a Monte Carlo assessment of their finite sample properties is reported. Two longitudinal studies are used for illustration.

A modified pseudolikelihood approach for analysis of longitudinal data

We consider the analysis of longitudinal data when the covariance function is modeled by additional parameters to the mean parameters. In general, inconsistent estimators of the covariance (variance/correlation) parameters will be produced when the "working" correlation matrix is misspecified, which may result in great loss of efficiency of the mean parameter estimators (albeit the consistency is preserved). We consider using different "Working" correlation models for the variance and the mean parameters. In particular, we find that an independence working model should be used for estimating the variance parameters to ensure their consistency in case the correlation structure is misspecified. The designated "working" correlation matrices should be used for estimating the mean and the correlation parameters to attain high efficiency for estimating the mean parameters. Simulation studies indicate that the proposed algorithm performs very well. We also applied different estimation procedures to a data set from a clinical trial for illustration.

A quasi-likelihood method for fractal-dimension estimation

We propose a simple method of constructing quasi-likelihood functions for dependent data based on conditional-mean-variance relationships, and apply the method to estimating the fractal dimension from box-counting data. Simulation studies were carried out to compare this method with the traditional methods. We also applied this technique to real data from fishing grounds in the Gulf of Carpentaria, Australia

Robust estimation using the Huber function with a data-dependent tuning constant

Robust estimation often relies on a dispersion function that is more slowly varying at large values than the square function. However, the choice of tuning constant in dispersion functions may impact the estimation efficiency to a great extent. For a given family of dispersion functions such as the Huber family, we suggest obtaining the "best" tuning constant from the data so that the asymptotic efficiency is maximized. This data-driven approach can automatically adjust the value of the tuning constant to provide the necessary resistance against outliers. Simulation studies show that substantial efficiency can be gained by this data-dependent approach compared with the traditional approach in which the tuning constant is fixed. We briefly illustrate the proposed method using two datasets.

Kallikrein-related peptidases in prostate cancer: From molecular function to clinical application

Prostate cancer is a leading contributor to male cancer-related deaths worldwide. Kallikrein-related peptidases (KLKs) are serine proteases that exhibit deregulated expression in prostate cancer, with KLK3, or prostate specific antigen (PSA), being the widely-employed clinical biomarker for prostate cancer. Other KLKs, such as KLK2, show promise as prostate cancer biomarkers and, additionally, their altered expression has been utilised for the design of KLK-targeted therapies. There is also a large body of in vitro and in vivo evidence supporting their role in cancer-related processes. Here, we review the literature on studies to date investigating the potential of other KLKs, in addition to PSA, as biomarkers and in therapeutic options, as well as their current known functional roles in cancer progression. Increased knowledge of these KLK-mediated functions, including degradation of the extracellular matrix, local invasion, cancer cell proliferation, interactions with fibroblasts, angiogenesis, migration, bone metastasis and tumour growth in vivo, may help define new roles as prognostic biomarkers and novel therapeutic targets for this cancer.

Evaluation of growth and changes in body composition following neonatal diagnosis of cystic fibrosis

Early deficits in nutritional status that might require specific treatment and early response to nutritional therapy were studied longitudinally in 25 infants with cystic fibrosis (CF) diagnosed by neonatal screening, using anthropometric and research body composition methodology, and evaluation of pancreatic function. At the time of confirmed diagnosis (mean 5.4 weeks), body mass, length, total body fat (TBF), and total body potassium (TBK) were all significantly reduced. Following diagnosis and commencement of therapy there was a normalization of weight, length, and TBK by 6-12 months of age, indicating catch-up growth. But in some individuals the response was incomplete, and as a group, mean total body fat remained significantly lower than normal at 1 year of age. Seven of 25 (28%) were pancreatic sufficient at diagnosis, and all but one had evidence of declining pancreatic function requiring the institution of pancreatic enzyme therapy during the next 1-9 months. The median age of commencement of enzyme therapy was 10 weeks (range 5 weeks to 11 months). These longitudinal assessments emphasize the dynamic changes occurring in absorptive function, body composition, and nutritional status following neonatal diagnosis of cystic fibrosis and may reflect previously described abnormalities of energy metabolism in this age group. Abnormal body composition is evident in most CF infants following diagnosis by neonatal screening but pancreatic damage may still be evolving. We suggest that early active nutritional therapy and surveillance for changes in pancreatic function are warranted in CF infants diagnosed by neonatal screening.

Exocrine pancreatic dysfunction in malnourished Australian Aboriginal children

Pancreatic exocrine dysfunction has been frequently recorded in protein-energy malnutrition in underdeveloped countries. In addition, the pancreas requires optimal nutrition for enzyme synthesis and potentially correctable pancreatic enzyme insufficiency may play a role in the continuation of protein-energy malnutrition. This problem has not been previously evaluated in Australian Aborigines. We have applied a screening test for pancreatic dysfunction (human immunoreactive trypsinogen [IRT] assay) to the study of 398 infants (6-36 months) admitted to the Alice Springs Hospital over a 20-month period. All infants were assessed by anthropometric measures and were assigned to to three nutritional groups (normal, moderate or severely malnourished) and two growth groups (stunted or not stunted). Of the 198 infants who had at least a single serum cationic trypsinogen measurement taken, normal values for serum IRT (with confidence limits) were obtained from 57 children, who were normally nourished. IRT levels were significantly correlated with the degree of underweight but there was no correlation with the degree of stunting or age. Mean IRT levels for the moderate and severely underweight groups were significantly greater than the mean for the normal group (P < 0.01). Seventeen children (8.6%) had trypsinogen levels in excess of the 95th percentile for the normally nourished group, reflecting acinar cell damage or ductal obstruction. We conclude that pancreatic dysfunction may be a common and important overlooked factor contributing to ongoing malnutrition and diseases in malnourished Australian Aboriginal children.

Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance

The method of generalised estimating equations for regression modelling of clustered outcomes allows for specification of a working matrix that is intended to approximate the true correlation matrix of the observations. We investigate the asymptotic relative efficiency of the generalised estimating equation for the mean parameters when the correlation parameters are estimated by various methods. The asymptotic relative efficiency depends on three-features of the analysis, namely (i) the discrepancy between the working correlation structure and the unobservable true correlation structure, (ii) the method by which the correlation parameters are estimated and (iii) the 'design', by which we refer to both the structures of the predictor matrices within clusters and distribution of cluster sizes. Analytical and numerical studies of realistic data-analysis scenarios show that choice of working covariance model has a substantial impact on regression estimator efficiency. Protection against avoidable loss of efficiency associated with covariance misspecification is obtained when a 'Gaussian estimation' pseudolikelihood procedure is used with an AR(1) structure.