When supply does not meet demand-ER stress and plant programmed cell death

The endoplasmic reticulum (ER) is the central organelle in the eukaryotic secretory pathway. The ER functions in protein synthesis and maturation and is crucial for proper maintenance of cellular homeostasis and adaptation to adverse environments. Acting as a cellular sentinel, the ER is exquisitely sensitive to changing environments principally via the ER quality control machinery. When perturbed, ER-stress triggers a tightly regulated and highly conserved, signal transduction pathway known as the unfolded protein response (UPR) that prevents the dangerous accumulation of unfolded/misfolded proteins. In situations where excessive UPR activity surpasses threshold levels, cells deteriorate and eventually trigger programmed cell death (PCD) as a way for the organism to cope with dysfunctional or toxic signals. The programmed cell death that results from excessive ER stress in mammalian systems contributes to several important diseases including hypoxia, neurodegeneration, and diabetes. Importantly, hallmark features and markers of cell death that are associated with ER stress in mammals are also found in plants. In particular, there is a common, conserved set of chaperones that modulate ER cell death signaling. Here we review the elements of plant cell death responses to ER stress and note that an increasing number of plant-pathogen interactions are being identified in which the host ER is targeted by plant pathogens to establish compatibility.

Catchment-specific element signatures in estuarine crocodiles (Crocodylus porosus) from the Alligator Rivers Region, northern Australia

The concentrations of Na, K, Ca, Mg, Ba, Sr, Fe, Al, Mn, Zn, Pb, Cu, Ni, Cr, Co, Se, U and Ti were determined in the osteoderms and/or flesh of estuarine crocodiles (Crocodylus porosus) captured in three adjacent catchments within the Alligator Rivers Region (ARR) of northern Australia. Results from multivariate analysis of variance showed that when all metals were considered simultaneously, catchment effects were significant (P≤0.05). Despite considerable within-catchment variability, linear discriminant analysis (LDA) showed that differences in elemental signatures in the osteoderms and/or flesh of C. porosus amongst the catchments were sufficient to classify individuals accurately to their catchment of occurrence. Using cross-validation, the accuracy of classifying a crocodile to its catchment of occurrence was 76% for osteoderms and 60% for flesh. These data suggest that osteoderms provide better predictive accuracy than flesh for discriminating crocodiles amongst catchments. There was no advantage in combining the osteoderm and flesh results to increase the accuracy of classification (i.e. 67%). Based on the discriminant function coefficients for the osteoderm data, Ca, Co, Mg and U were the most important elements for discriminating amongst the three catchments. For flesh data, Ca, K, Mg, Na, Ni and Pb were the most important metals for discriminating amongst the catchments. Reasons for differences in the elemental signatures of crocodiles between catchments are generally not interpretable, due to limited data on surface water and sediment chemistry of the catchments or chemical composition of dietary items of C. porosus. From a wildlife management perspective, the provenance or source catchment(s) of 'problem' crocodiles captured at settlements or recreational areas along the ARR coastline may be established using catchment-specific elemental signatures. If the incidence of problem crocodiles can be reduced in settled or recreational areas by effective management at their source, then public safety concerns about these predators may be moderated, as well as the cost of their capture and removal. Copyright © 2002 Elsevier Science B.V.

A survey in mathematics for industry: Two-timing and matched asymptotic expansions for singular perturbation problems

Following the derivation of amplitude equations through a new two-time-scale method [O'Malley, R. E., Jr. & Kirkinis, E (2010) A combined renormalization group-multiple scale method for singularly perturbed problems. Stud. Appl. Math. 124, 383-410], we show that a multi-scale method may often be preferable for solving singularly perturbed problems than the method of matched asymptotic expansions. We illustrate this approach with 10 singularly perturbed ordinary and partial differential equations. © 2011 Cambridge University Press.

Amplitude equations and asymptotic expansions for multi-scale problems

In this paper we introduce a new technique to obtain the slow-motion dynamics in nonequilibrium and singularly perturbed problems characterized by multiple scales. Our method is based on a straightforward asymptotic reduction of the order of the governing differential equation and leads to amplitude equations that describe the slowly-varying envelope variation of a uniformly valid asymptotic expansion. This may constitute a simpler and in certain cases a more general approach toward the derivation of asymptotic expansions, compared to other mainstream methods such as the method of Multiple Scales or Matched Asymptotic expansions because of its relation with the Renormalization Group. We illustrate our method with a number of singularly perturbed problems for ordinary and partial differential equations and recover certain results from the literature as special cases. © 2010 - IOS Press and the authors. All rights reserved.

Simulation of ion flux distribution in conductive and nonconductive nanotip patterns

The distribution of flux of carbon-bearing cations over nanopatterned surfaces with conductive nanotips and nonconductive nanoislands is simulated using the Monte-Carlo technique. It is shown that the ion current is focused to nanotip surfaces when the negative substrate bias is low and only slightly perturbed at higher substrate biases. In the low-bias case, the mean horizontal ion displacement caused by the nanotip electric field exceeds 10 nm. However, at higher substrate biases, this value reduces down to 2 nm. In the nonconductive nanopattern case, the ion current distribution is highly nonuniform, with distinctive zones of depleted current density around the nanoislands. The simulation results suggest the efficient means to control ion fluxes in plasma-aided nanofabrication of ordered nanopatterns, such as nanotip microemitter structures and quantum dot or nanoparticle arrays. © World Scientific Publishing Company.

Unbalance identification in large steam turbo-generator unit using a model-based method

Fault identification in industrial machine is a topic of major importance under engineering point of view. In fact, the possibility to identify not only the type, but also the severity and the position of a fault occurred along a shaft-line allows quick maintenance and shorten the downtime. This is really important in the power generation industry where the units are often of several tenths of meters long and where the rotors are enclosed by heavy and pressure-sealed casings. In this paper, an industrial experimental case is presented related to the identification of the unbalance on a large size steam turbine of about 1.3 GW, belonging to a nuclear power plant. The case history is analyzed by considering the vibrations measured by the condition monitoring system of the unit. A model-based method in the frequency domain, developed by the authors, is introduced in detail and it is then used to identify the position of the fault and its severity along the shaft-line. The complete model of the unit (rotor – modeled by means of finite elements, bearings – modeled by linearized damping and stiffness coefficients and foundation – modeled by means of pedestals) is analyzed and discussed before being used for the fault identification. The assessment of the actual fault was done by inspection during a scheduled maintenance and excellent correspondence was found with the identified one by means of authors’ proposed method. Finally a complete discussion is presented about the effectiveness of the method, even in presence of a not fine tuned machine model and considering only few measuring planes for the machine vibration.

Prediction of thermal expansion properties of carbon nanotubes using molecular dynamics simulations

The axial coefficients of thermal expansion (CTE) of various carbon nanotubes (CNTs), i.e., single-wall carbon nanotubes (SWCNTs), and some multi-wall carbon nanotubes (MWCNTs), were predicted using molecular dynamics (MDs) simulations. The effects of two parameters, i.e., temperature and the CNT diameter, on CTE were investigated extensively. For all SWCNTs and MWCNTs, the obtained results clearly revealed that within a wide low temperature range, their axial CTEs are negative. As the diameter of CNTs decreases, this temperature range for negative axial CTEs becomes narrow, and positive axial CTEs appear in high temperature range. It was found that the axial CTEs vary nonlinearly with the temperature, however, they decrease linearly as the CNT diameter increases. Moreover, within a wide temperature range, a set of empirical formulations was proposed for evaluating the axial CTEs of armchair and zigzag SWCNTs using the above two parameters. Finally, it was found that the absolute value of the negative axial CTE of any MWCNT is much smaller than those of its constituent SWCNTs, and the average value of the CTEs of its constituent SWCNTs. The present fundamental study is very important for understanding the thermal behaviors of CNTs in such as nanocomposite temperature sensors, or nanoelectronics devices using CNTs.

Optimisation methods for coupled thermodynamic and 1D design of radial-inflow turbines

Optimisation is a fundamental step in the turbine design process, especially in the development of non-classical designs of radial-inflow turbines working with high-density fluids in low-temperature Organic Rankine Cycles (ORCs). The present work discusses the simultaneous optimisation of the thermodynamic cycle and the one-dimensional design of radial-inflow turbines. In particular, the work describes the integration between a 1D meanline preliminary design code adapted to real gases and the performance estimation approach for radial-inflow turbines in an established ORC cycle analysis procedure. The optimisation approach is split in two distinct loops; the inner operates on the 1D design based on the parameters received from the outer loop, which optimises the thermodynamic cycle. The method uses parameters including brine flow rate, temperature and working fluid, shifting assumptions such as head and flow coefficients into the optimisation routine. The discussed design and optimisation method is then validated against published benchmark cases. Finally, using the same conditions, the coupled optimisation procedure is extended to the preliminary design of a radial-inflow turbine with R143a as working fluid in realistic geothermal conditions and compared against results from commercially-available software RITAL from Concepts-NREC.

Simultaneous localization and planning on multiple map hypotheses

This paper presents a novel method to rank map hypotheses by the quality of localization they afford. The highest ranked hypothesis at any moment becomes the active representation that is used to guide the robot to its goal location. A single static representation is insufficient for navigation in dynamic environments where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners. In our approach we simultaneously rank multiple map hypotheses by the influence that localization in each of them has on locally accurate odometry. This is done online for the current locally accurate window by formulating a factor graph of odometry relaxed by localization constraints. Comparison of the resulting perturbed odometry of each hypothesis with the original odometry yields a score that can be used to rank map hypotheses by their utility. We deploy the proposed approach on a real robot navigating a structurally noisy office environment. The configuration of the environment is physically altered outside the robots sensory horizon during navigation tasks to demonstrate the proposed approach of hypothesis selection.

The influence of atmospheric conditions on the leakage current of ceramic insulators on the Colombian Caribbean coast

The contamination of electrical insulators is one of the major contributors to the risk of operation outages in electrical substations, especially in coastal zones with high salinity levels and atmospheric pollution. By using the measurement of leakage-currents, which is one of the main indicators of contamination in insulators, this work seeks to the determine the correlation with climatic variables, such as ambient temperature, relative humidity, solar irradiance, atmospheric pressure, and wind speed and direction. The results obtained provide an input to the behaviour of the leakage current under atmospheric conditions that are particular to the Caribbean coast of Colombia. Spearman’s rank correlation coefficients and principal component analysis are utilised to determine the significant relationships among the different variables under consideration. The necessary information for the study was obtained via historical databases of both atmospheric variables and the leakage current measured in over a period of one year in a 220-kV potential transformer insulator. We identified the influencing factors of temperature, humidity, radiation, wind speed and direction on the magnitude of the leakage current as the most relevant.

Building macroscale models from microscale probabilistic models : a general probabilistic approach for nonlinear diffusion and multispecies phenomena

A discrete agent-based model on a periodic lattice of arbitrary dimension is considered. Agents move to nearest-neighbor sites by a motility mechanism accounting for general interactions, which may include volume exclusion. The partial differential equation describing the average occupancy of the agent population is derived systematically. A diffusion equation arises for all types of interactions and is nonlinear except for the simplest interactions. In addition, multiple species of interacting subpopulations give rise to an advection-diffusion equation for each subpopulation. This work extends and generalizes previous specific results, providing a construction method for determining the transport coefficients in terms of a single conditional transition probability, which depends on the occupancy of sites in an influence region. These coefficients characterize the diffusion of agents in a crowded environment in biological and physical processes.

Exploring Retinal Markers of Diabetic Neuropathy

Purpose : To investigate the application of retinal nerve fibre layer (RNFL) thickness as a marker for severity of diabetic peripheral neuropathy (DPN) in people with Type 2 diabetes. Methods : This was a cross-sectional study whereby 61 participants (mean age 61 [41-75 years], mean duration of diabetes 14 [1-40 years], 70% male) with Type 2 diabetes and DPN underwent optical coherence tomography (OCT) scans. Global and 4 quadrant (TSNI) RNFL thicknesses were measured at 3.45mm around the optic nerve head of one eye. Neuropathy disability score (NDS) was used to assess the severity of DPN on a 0 to 10 scale. Participants were divided into three age-matched groups representing mild (NDS=3-5), moderate (NDS=6-8) and severe (NDS=9-10) neuropathy. Two regression models were fitted for statistical analysis: 1) NDS scores as co-variate for global and quadrant RNFL thicknesses, 2) NDS groups as a factor for global RNFL thickness only. Results : Mean (SD) RNFL thickness (µm) was 103(9) for mild neuropathy (n=34), 101(10) for moderate neuropathy (n=16) and 95(13) in the group with severe neuropathy (n=11). Global RNFL thickness and NDS scores were statistically significantly related (b=-1.20, p=0.048). When neuropathy was assessed across groups, a trend of thinner mean RNFL thickness was observed with increasing severity of neuropathy; however, this result was not statistically significant (F=2.86, p=0.065). TSNI quadrant analysis showed that mean RNFL thickness reduction in the inferior quadrant was 2.55 µm per 1 unit increase in NDS score (p=0.005). However, the regression coefficients were not statistically significant for RNFL thickness in the superior (b=-1.0, p=0.271), temporal (b=-0.90, p=0.238) and nasal (b=-0.99, p=0.205) quadrants. Conclusions : RNFL thickness was reduced with increasing severity of DPN and the effect was most evident in the inferior quadrant. Measuring RNFL thickness using OCT may prove to be a useful, non-invasive technique for identifying severity of DPN and may also provide additional insight into common mechanisms for peripheral neuropathy and RNFL damage.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Assessing uncertainty in pollutant wash-off modelling via model validation

Stormwater pollution is linked to stream ecosystem degradation. In predicting stormwater pollution, various types of modelling techniques are adopted. The accuracy of predictions provided by these models depends on the data quality, appropriate estimation of model parameters, and the validation undertaken. It is well understood that available water quality datasets in urban areas span only relatively short time scales unlike water quantity data, which limits the applicability of the developed models in engineering and ecological assessment of urban waterways. This paper presents the application of leave-one-out (LOO) and Monte Carlo cross validation (MCCV) procedures in a Monte Carlo framework for the validation and estimation of uncertainty associated with pollutant wash-off when models are developed using a limited dataset. It was found that the application of MCCV is likely to result in a more realistic measure of model coefficients than LOO. Most importantly, MCCV and LOO were found to be effective in model validation when dealing with a small sample size which hinders detailed model validation and can undermine the effectiveness of stormwater quality management strategies.

Zinc oxide quantum dot nanostructures

Zinc oxide (ZnO) is one of the most intensely studied wide band gap semiconductors due to its many desirable properties. This project established new techniques for investigating the hydrodynamic properties of ZnO nanoparticles, their assembly into useful photonic structures, and their multiphoton absorption coefficients for excitation with visible or infrared light rather than ultraviolet light. The methods developed are also applicable to a wide range of nanoparticle samples.