Reduced brain serotonin transporter availability in major depression as measured by [123I]-2 beta-carbomethoxy-3 beta-(4-iodophenyl)tropane and single photon emission computed tomography

BACKGROUND: Prior research has suggested reductions in the density of serotonin transporter (SERT) binding sites in blood platelets and post-mortem brain tissue of depressed patients. We sought to determine whether patients with unipolar major depression have diminished SERT availability as assessed by both brainstem [123I] beta-CIT SPECT and platelet [3H]paroxetine binding. METHODS: Drug-free depressed and healthy subjects were injected with 211 +/- 22 MBq [123I] beta-CIT and imaged 24 +/- 2 h later under equilibrium conditions. A ratio of specific to nonspecific brain uptake (V3" = (brainstem-occipital)/occipital), a measure proportional to the binding potential (Bmax/Kd), was used for all comparisons. RESULTS: Results showed a statistically significant reduction in brainstem V3" values in depressed as compared to healthy subjects (3.1 +/- .9 vs. 3.8 +/- .8, p = .02). Platelet [3H]paroxetine binding was not altered (Bmax = 2389 +/- 484 vs. 2415 +/- 538 fmol/mg protein, p = .91) and was not significantly correlated with brainstem [123I] beta-CIT binding (r = -0.14, p = .48). CONCLUSIONS: These data are the first to suggest reductions in the density of brain SERT binding sites in living depressed patients. These findings provide further support for a preeminent role for alterations in serotonergic neurons in the pathophysiology of depression.     

The Internet & Healthcare Education: HELIX

With the advent of the World Wide Web (WWW), we are now on the cusp of a revolution in computer technology that will dramatically enhance medical education. An historical analogy might be Johann Gutenberg's invention of movable type in the 1400's-radically decreasing the cost, time, and expertise required to reproduce printed materials. Now, the WWW can decrease the cost of disseminating medical educational materials. When an educational module is authored for the Web, it can be placed on a computer "server" which in turn, distributes the program on the WWW to anyone with a computer and Internet access. Rapidly emerging standards are being developed to allow increasingly rich educational experiences on the Internet. With the introduction of HTML (hypertext markup language), a standardized method of placing text and graphics, as well as the connections between them, was created.     

Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.     

A multi-strategy knowledge interoperability framework for heterogeneous learning objects

This paper presents a knowledge exchange framework that can leverage the interoperability among semantically heterogeneous learning objects. With the release of various e-Learning standards, learning contents and digital courses are easy to achieve cross-platform sharing, exchanging, and even reorganizing. However, knowledge sharing in semantic level is still a challenge due to that the learning materials can be presented in any form, such as audios, videos, web pages, and even flash files. The proposed knowledge exchange framework allows users to share their learning materials (also called “learning objects”) in semantic level automatically. This framework contains two methodologies: the first is a semantic mapping between knowledge bases (i.e. ontologies) which have essentially similar concepts, and the second is an ontology-based classification algorithm for sharable learning objects. The proposed algorithm adopts the IMS DRI standard and classifies the sharable learning objects from heterogeneous repositories into a local knowledge base by their inner meaning instead of keyword matching. Significance of this research lies in the semantic inferring rules for ontology mapping and learning objects classification as well as the full automatic processing and self-optimizing capability. Focused on digital learning materials and contrasted to other traditional technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.     

Multisensor based security robot system for intelligent building

Intelligent building can provide safety, convenience, efficiency and entertainment for life in the 21st century. The most importance role of the intelligent building is the security system. We develop a multi sensor-based intelligent security robot (ISR) that is widely employed in intelligent buildings. The intelligent security robot can detect abnormal and dangerous situations and notify users. The robot has the shape of cylinder and its diameter, height, and weight are 50 cm, 130 cm and 100 kg respectively. The function of the ISR contains six parts. There is the software development system; avoiding obstacle and motion planning system, image system, sensor system, remote supervise system and other systems. We develop a multi sensor-based sensor system in the ISR. We use multiple multisensor fusion algorithms to get an exact decision in the detection subsystem of the sensor system. There is an adaptive fusion method, a rule based method, and a statistical signal method. We demonstrate the remote supervisory system to control the ISR using a direct control mode and a behavior control mode. We think that the man–machine interface in a security robot system must have mobility and convenience. Therefore, we use a touch screen to display the system state, and design a general user interface (GUI) to service the user and visitors. The user can remotely control the appliance using a cell phone through a GSM modem, too. The appliance module can feedback reaction results to the user through a cell phone. Finally, we implement the fire detection system in the intelligent security robot (Chung-Cheng-I). If a fire occurs, the intelligent security robot can find out the fire source using the fire detection system. In intruder detection, we program the same scenario to detect the intruder using the intelligent security robot. The intelligent security robot transmits the message of the detection result to the user using a GSM modem for a fire event or intruder, and transmits the detection result to a client computer through the internet.     

Data hiding in AMBTC images using quantization level modification and perturbation technique

Multimedia Tools and Applications - A novel data hiding method for Absolute Moment Block Truncation Coding (AMBTC) compressed image based on quantization level modification is proposed. Blocks of...     

Fabrication and characteristic detection of graphene nanoelectrodes

Graphene has the advantages of high electrical conductivity, high heat conductivity, and low noise, which makes it a potential option for integrated circuits interconnection and nanoelectrodes. In this paper, we present a novel fabrication method for graphene nanoeletrodes with nanogap. First, graphene grown by chemical vapor deposition (CVD) is assembled to a chip with microelectrodes. Second, an atomic force microscopy (AFM) based mechanical cutting method is developed to cut the graphene into nanoribbons and nanoeletrodes with nanogap. Then the electronic property of a single nanodot is characterized using the garphene nanoelectrodes, demonstrating the effectiveness of the graphene nanoelectrodes. The fabricated graphene nanoeletrode pairs can be used as probes to detect single molecule in micro-environment, and show an attractive prospect for future molecular electronics applications.     

SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique.