Effect of deep brain stimulation on nucleus accumbens dopamine in a preclinicla model of antidepressant treatment-resistance

Background / Purpose: To determine if clinically effective deep brain stimulation (DBS) of neurosurgical targets for treatment-resistant depression regulates transient mesoaccumbens dopamine release in control and antidepressant-resistant animals (rats).

Main conclusion: In control rats, DBS stimulation of either the nucleus accumbens or infralimbic cortex significantly attenuated transient mesoaccumbens dopamine efflux, with nucleus accumbens DBS inducing a greater attenuation than infralimbic DBS. High frequency DBS of both targets induced long-term depression of transient accumbens dopamine release, lasting > 2hr post DBS.

Conversely, in antidepressant-resistant rats, infralimbic DBS significantly potentiated transient mesoaccumbens dopamine efflux during stimulation, but failed to induce long-lasting changes in neurotransmission. This suggests that a key mechanism of DBS for treatment-resistant depression is the regulation of dysfunctional mesoaccumbens dopamine neurotransmission.

A hybrid neural network model for noisy data regression

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.

Power penalty assessment of OXADM device model - analytical analysis

This OXADM are located in the nodes, which have more than two switching directions in ring networks. The function of OXADM is to flexibility switch the wavelengths among the different input and output ports. Because of the OXADM's imperfect performance, the insertion loss and crosstalk are induced in the system. Analytical modeling method is using to analyze the OXADM structure in crosstalk or power leakage that lead to the power penalty. To overcome this problem, power penalty is needed to be supplied. The insertion of this power penalty depends on few parameters. The parameters that we going to investigate here will be in term of number of operating wavelengths and number of input/output ports as well as the Q factor. The variation of this parameters will affects the amount of the desired power penalty. Simulation results in higher crosstalk or higher power penalty needed as the number of OXADM increases. As the sum of the wavelength and the number of input/output for each OXADM increases, the power penalty will increased as well. Investigation on the maximum Q factors is 6 to get the minimum power penalty at the lowest BER for most of the combination of the sum of the wavelength and the number of input/output for each OXADM.

Law, governance and risk: deconstructing the public-private divide in climate change adaptation

The article focuses on the public–private divide in law and which organizes principles for and governance. It analyzes the governance model of public–private divide regarding for climate change adaptation in context to a case study of water governance and flood risk. It compares the relationship between state and individual laws which helps in policy setting.

A fractional cointegration approach to testing the Ohlson accounting based valuation model

We examine the long-run relationship between market value, book value, and residual income in the Ohlson (Contemp Acc Res 11(2):661-687, 1995) model. In particular, we test if market value is cointegrated with book value and residual income in light of their non-stationary behaviors. We find that cointegration applies to only 51 % of the sample firms, casting doubt that book value and residual income alone are adequate in tracking variations in market value, yet we find that market value is fractional cointegrated with book value and residual income for 89 % of the sample firms. This implies that the long-run relationship follows a slow but mean-reverting process. Our results therefore support the Ohlson model. © 2012 Springer Science+Business Media New York.

Enhancing an evolving tree-based text document visualization model with fuzzy c-Means clustering

An improved evolving model, i.e., Evolving Tree (ETree) with Fuzzy c-Means (FCM), is proposed for undertaking text document visualization problems in this study. ETree forms a hierarchical tree structure in which nodes (i.e., trunks) are allowed to grow and split into child nodes (i.e., leaves), and each node represents a cluster of documents. However, ETree adopts a relatively simple approach to split its nodes. Thus, FCM is adopted as an alternative to perform node splitting in ETree. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows that the proposed ETree-FCM model is effective for undertaking text document clustering and visualization problems.

Topic model kernel : an empirical study towards probabilistically reduced features for classification

Probabilistic topic models have become a standard in modern machine learning with wide applications in organizing and summarizing ‘documents’ in high-dimensional data such as images, videos, texts, gene expression data, and so on. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics than bag-of-word interpretation, but also more informative for classification tasks. This paper describes the Topic Model Kernel (TMK), a high dimensional mapping for Support Vector Machine classification of data generated from probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks from real world datasets. We outperform existing kernels on the distributional features and give the comparative results on non-probabilistic data types.