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Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existence (or not) of infinite clusters is explored for two stochastic models of intersecting line segments in d ≥ 2 dimensions. Salient features of the phase diagram are established in each case. The models are based on site percolation on ℤ d with parameter p ∈ (0,1]. For each occupied site v, and for each of the 2d possible coordinate directions, declare the entire line segment from v to the next occupied site in the given direction to be either blue or not blue according to a given stochastic rule. In the ‘one-choice model’, each occupied site declares one of its 2d incident segments to be blue. In the ‘independent model’, the states of different line segments are independent. [ABSTRACT FROM AUTHOR]

Copyright of SSEM-Journal is the property of Schwiez Zschr Sportmed Sporttraumatologie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The number of studies investigating links between among-individual differences in metabolic rate (MR) and behavior has grown dramatically in the past several years. A major and often untested assumption of these studies is that the selected measure of MR is a valid proxy for energetic constraints. We argue that without explicitly testing this assumption, observed patterns between MR and behavior are uninterpretable. We provide guidelines for evaluating how a given measure of MR relates to constraints on the acquisition or expenditure of energy. The approach we advocate will allow researchers to develop and test a priori predictions relating energy metabolism and behavior. We conclude by highlighting several exciting directions for future work in this rapidly growing field. [ABSTRACT FROM AUTHOR]

We propose a method for estimating the parameters of contingency table models, which is motivated by a geometrical idea. Our method--bisector regression for contingency tables (BRCT)--is based on a nested structure of contingency table models. Our method estimates parameters corresponding to the interactions of lower orders after estimating or eliminating those of higher orders. BRCTgenerates a sequence of parameter estimates, each element of which represents a model and a parameter estimate. The length of the sequence is equal to the number of parameters, which is much smaller than the total number of models. We describe the BRCTalgorithm and show an example. We provide explanations for two cases: (a) two factors and (b) K factors. [ABSTRACT FROM AUTHOR]

Abstract: Neural networks have been successfully used to model nonlinear dynamic systems. However, when a static neural network model is used in system fault detection and the model prediction error is used as the residual, the residual is insensitive to the fault if the neural network used is in dependent mode. This paper proposed the use of a radial basis function network in independent mode as the system model for fault detection, and it was found that the residual is sensitive to the fault. To enhance the signal to noise ratio of the detection the recursive orthogonal least squares algorithm is employed to train the network weights. Another radial basis function network is used to isolate fault using the information in the residual signal. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at the Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations. [Copyright &y& Elsevier]

We propose a new modeling approach for inspection data that provides a more useful interpretation of the patterns of detections of invasive pests, using cargo inspection as a motivating example. Methods that are currently in use generally classify shipments according to their likelihood of carrying biosecurity risk material, given available historical and contextual data. Ideally, decisions regarding which cargo containers to inspect should be made in real time, and the models used should be able to focus efforts when the risk is higher. In this study, we propose a dynamic approach that treats the data as a time series in order to detect periods of high risk. A regulatory organization will respond differently to evidence of systematic problems than evidence of random problems, so testing for serial correlation is of major interest. We compare three models that account for various degrees of serial dependence within the data. First is the independence model where the prediction of the arrival of a risky shipment is made solely on the basis of contextual information. We also consider a Markov chain that allows dependence between successive observations, and a hidden Markov model that allows further dependence on past data. The predictive performance of the models is then evaluated using ROC and leakage curves. We illustrate this methodology on two sets of real inspection data. [ABSTRACT FROM AUTHOR]

Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity. Based on the biomass data of 137 young trees in five typical plantations on the Tibetan Plateau, independent biomass models were developed using the weighted generalized least squares method, with basic diameter as the predictor instead of DBH. Additive biomass models of controlling directly by proportion functions and controlling by the sum of equations were selected. Additive biomass models for the whole plant and each component were developed by applying weighted nonlinear seemingly uncorrelated regression. The results showed that the binary additive biomass model ( R 2 reached 0.90-0.99) performed better than the monadic biomass models and independent biomass models for the estimation of total biomass. For different tree species, two forms of the additive models had their own advantages, with neglectable difference in accuracy. From the perspective of forestry production, models of controlling directly by proportion functions were more practical. From the perspective of predictors extraction by remote sensing technology, suitable young tree biomass models were developed for remote sensing estimation. In this study, the additive model had high overall fitting accuracy and could accurately estimate the whole plant and component biomass of young trees in similar climatic environments.

Abstract: The RNA-binding protein TDP-43 is strongly linked to neurodegeneration. Not only are mutations in the gene encoding TDP-43 associated with ALS and FTLD, but this protein is also a major constituent of pathological intracellular inclusions in these diseases. Recent studies have significantly expanded our understanding of TDP-43 physiology. TDP-43 is now known to play important roles in neuronal RNA metabolism. It binds to and regulates the splicing and stability of numerous RNAs encoding proteins involved in neuronal development, synaptic function and neurodegeneration. Thus, a loss of these essential functions is an attractive hypothesis regarding the role of TDP-43 in neurodegeneration. Moreover, TDP-43 is an aggregation-prone protein and, given the role of toxic protein aggregates in neurodegeneration, a toxic gain-of-function mechanism is another rational hypothesis. Importantly, ALS related mutations modulate the propensity of TDP-43 to aggregate in cell culture. Several recent studies have documented that cytoplasmic TDP-43 aggregates co-localize with stress granule markers. Stress granules are cytoplasmic inclusions that repress translation of a subset of RNAs in times of cellular stress, and several proteins implicated in neurodegeneration (i.e. Ataxin-2 and SMN) interact with stress granules. Thus, understanding the interplay between TDP-43 aggregation, stress granules and the effect of ALS-associated TDP-43 mutations may be the key to understanding the role of TDP-43 in neurodegeneration. We propose two models of TDP-43 aggregate formation. The “independent model” stipulates that TDP-43 aggregation is independent of stress granule formation, in contrast to the “precursor model” which presents the idea that stress granule formation contributes to a TDP-43 aggregate “seed” and that chronic stress leads to concentration-dependent TDP-43 aggregation. This article is part of a Special Issue entitled: RNA-Binding Proteins. [Copyright &y& Elsevier]

We propose a new modeling approach for inspection data that provides a more useful interpretation of the patterns of detections of invasive pests, using cargo inspection as a motivating example. Methods that are currently in use generally classify shipments according to their likelihood of carrying biosecurity risk material, given available historical and contextual data. Ideally, decisions regarding which cargo containers to inspect should be made in real time, and the models used should be able to focus efforts when the risk is higher. In this study, we propose a dynamic approach that treats the data as a time series in order to detect periods of high risk. A regulatory organization will respond differently to evidence of systematic problems than evidence of random problems, so testing for serial correlation is of major interest. We compare three models that account for various degrees of serial dependence within the data. First is the independence model where the prediction of the arrival of a risky shipment is made solely on the basis of contextual information. We also consider a Markov chain that allows dependence between successive observations, and a hidden Markov model that allows further dependence on past data. The predictive performance of the models is then evaluated using ROC and leakage curves. We illustrate this methodology on two sets of real inspection data., (© 2013 Society for Risk Analysis.)