Neural Membrane Mutual Coupling Characterisation Using Entropy-Based Iterative Learning Identification

This paper investigates the interaction phenomena of the coupled axons while the mutual coupling factor is presented as a pairwise description. Based on the Hodgkin-Huxley model and the coupling factor matrix, the membrane potentials of the coupled myelinated/unmyelinated axons are quantified which implies that the neural coupling can be characterised by the presented coupling factor. Meanwhile the equivalent electric circuit is supplied to illustrate the physical meaning of this extended model. In order to estimate the coupling factor, a data-based iterative learning identification algorithm is presented where the Rényi entropy of the estimation error has been minimised. The convergence of the presented algorithm is analysed and the learning rate is designed. To verified the presented model and the algorithm, the numerical simulation results indicate the correctness and the effectiveness. Furthermore, the statistical description of the neural coupling, the approximation using ordinary differential equation, the measurement and the conduction of the nerve signals are discussed respectively as advanced topics. The novelties can be summarised as follows: 1) the Hodgkin-Huxley model has been extended considering the mutual interaction between the neural axon membranes, 2) the iterative learning approach has been developed for factor identification using entropy criterion, and 3) the theoretical framework has been established for this class of system identification problems with convergence analysis

Variance and Entropy Assignment for Continuous-Time Stochastic Nonlinear Systems

This paper investigates the randomness assignment problem for a class of continuous-time stochastic nonlinear systems, where variance and entropy are employed to describe the investigated systems. In particular, the system model is formulated by a stochastic differential equation. Due to the nonlinearities of the systems, the probability density functions of the system state and system output cannot be characterised as Gaussian even if the system is subjected to Brownian motion. To deal with the non-Gaussian randomness, we present a novel backstepping-based design approach to convert the stochastic nonlinear system to a linear stochastic process, thus the variance and entropy of the system variables can be formulated analytically by the solving Fokker–Planck–Kolmogorov equation. In this way, the design parameter of the backstepping procedure can be then obtained to achieve the variance and entropy assignment. In addition, the stability of the proposed design scheme can be guaranteed and the multi-variate case is also discussed. In order to validate the design approach, the simulation results are provided to show the effectiveness of the proposed algorithm

MicroRNA-9-5p inhibits osteosarcoma cell promotion, metastasis and resistance to apoptosis via negatively targeting Grb2-associated binding protein 2

521-529The study explores the inhibition effects of MicroRNAs in osteosarcoma, as a means of suggesting it as treatment for bone cancer. MicroRNAs (miRNAs) are a sort of noncoding RNA molecules that regulates gene expression by targeting mRNAs and play critical roles in tumor development. This study probed the effect of miR-9-5p on osteosarcoma development. Human osteosarcoma cell lines U2-OS, 143B, MG63 and HOS and normal human osteoblast cell line hFOB were cultivated and expression of miR-9-5p and Grb2-associated binding protein 2 (Gab2) measured. The binding of miR-9-5p and Gab2 was confirmed using a bio-information program and dual luciferase reporter gene assay. Loss-of-functions of miR-9-5p and Gab2 were performed to measure their roles in osteosarcoma cell proliferation, invasion, migration and resistance to death. Result showed high miR-9-5p expression and low Gab2 expression in osteosarcoma cells, particularly in U2-OS cells. miR-9-5p directly bound to the 3¢untranslated region of Gab2. Down-regulated miR-9-5p induced U2-OS cell proliferation, invasion and the resistance to death, while conversely, silenced Gab2 led to an opposite trend on U2-OS cell growth and metastasis. Moreover, co-effect of inhibited miR-9-5p and silenced Gab2 led to decreased cell proliferation but promoted cell apoptosis compared to inhibited miR-9-5p alone, while it led to enhanced cell proliferation and invasion, but reduced cell apoptosis compared to silenced Gab2 alone. To conclude, this study demonstrated that miR-9-5p could inhibit osteosarcoma cell proliferation, invasion, migration and resistance to death via negatively targeting Gab2

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences

Target Localization and Sensor Movement Trajectory Planning with Bearing-Only Measurements in Three Dimensional Space

In order to improve the accuracy of bearing-only localization in three dimensional (3D) space, this paper proposes a novel bias compensation method and a new single-sensor maneuvering trajectory algorithm, respectively. Compared with traditional methods, the bias compensation method estimates the unknown variance of bearing noise consistently, which is utilized in pseudo-linear target localization to achieve higher precision. The sensor maneuvering algorithm designs the next moment sensor location in consideration of all the past sensor locations, unlike other approaches that only consider finite past locations. Research shows that the trajectories generated by our algorithm have greater Fisher information matrix (FIM) determinants and better localization accuracy