Successes and failures in assessing acquired language disorders using an Internet-based telerehabilitation system

We have used a telerehabilitation system (eREHAB) to remotely assess acquired language disorders via the Internet. The system was used to establish a 128 kbit/s videoconference between two sites and allowed a remote language assessment to be conducted using the standardized Boston Diagnostic Aphasia Examination (BDAE). The system had the capacity to display text and images, and could play pre-recorded instructions to the participant via various built-in tools. A touch screen allowed tasks involving picture identification to be completed easily. Eighteen participants with a diagnosis of an acquired language disorder were simultaneously assessed using the eREHAB system, and in the traditional face-to-face manner by two speech pathologists. There was very high agreement between the two assessors, with weighted kappa scores of 0.8–1.0 for 88% of the sub-tests of the BDAE. There was also high agreement (80–100%) and high kappa scores (0.67–0.90) between assessors on the six rating scales relating to language characteristics. The agreement between the two assessors for the diagnosis of the type of aphasia was 83%. Limitations of the system related mainly to problems inherent in IP videoconferencing. The inability to maintain the preferred speed of 128 kbit/s for the duration of the videoconference and the resultant increase in video and audio breakup and latency affected the clinician’s ability to administer the BDAE with the same ease and accuracy as in face-to-face administration. These difficulties were exacerbated when participants presented with a moderate to severe language disorder, auditory comprehension deficits or significant hearing loss. Despite these limitations, a valid assessment of language disorder was found to be feasible via this telerehabilitation application.

Design and development of agent-based procurement system to enhance business intelligence

The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.

Design and development of an agent-based information system for process improvement

To meet changing needs of customers and to survive in the increasingly globalised and competitive environment, it is necessary for companies to equip themselves with intelligent tools, thereby enabling managerial levels to use the tactical decision in a better way. However, the implementation of an intelligent system is always a challenge in Small- and Medium-sized Enterprises (SMEs). Therefore, a new and simple approach with 'process rethinking' ability is proposed to generate ongoing process improvements over time. In this paper, a roadmap of the development of an agent-based information system is described. A case example has also been provided to show how the system can assist non-specialists, for example, managers and engineers to make right decisions for a continual process improvement. Copyright © 2006 Inderscience Enterprises Ltd.

Increased potential of a cationic liposome-based delivery system:enhancing stability and sustained immunological activity in pre-clinical development

The combination of dimethyl dioctadecyl ammonium bromide (DDA) and the synthetic cord factor trehalose dibehenate (TDB) with Ag85B-ESAT-6 (H1 fusion protein) has been found to promote strong protective immune responses against Mycobacterium tuberculosis. The development of a vaccine formulation that is able to facilitate the requirements of sterility, stability and generation of a vaccine product with acceptable composition, shelf-life and safety profile may necessitate selected alterations in vaccine formulation. This study describes the implementation of a sterilisation protocol and the use of selected lyoprotective agents in order to fulfil these requirements. Concomitantly, close analysis of any alteration in physico-chemical characteristics and parameters of immunogenicity have been examined for this promising DDA liposome-based tuberculosis vaccine. The study addresses the extensive guidelines on parameters for non-clinical assessment, suitable for liposomal vaccines and other vaccine delivery systems issued by the World Health Organisation (WHO) and the European Medicines Agency (EMEA). Physical and chemical stability was observed following alteration in formulations to include novel cryoprotectants and radiation sterilisation. Immunogenicity was maintained following these alterations and even improved by modification with lysine as the cryoprotective agent for sterilised formulations. Taken together, these results outline the successful alteration to a liposomal vaccine, representing improved formulations by rational modification, whilst maintaining biological activity.

The development and application of a coding and classification system for manufacturing industry

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Associação entre comprometimento motor, mobilidade e qualidade de vida em crianças com paralisia cerebral espástica, nível I, II e III do Gross Motor Function Classification System

The objective of this study was to verify the association between some mobility items of the International Classification Functionality (ICF), with the evaluations Gross Motor Function Measure (GMFM-88), 1-minute walk test (1MWT) and if the motor impairment influences the quality of life in children with Cerebral Palsy (PC), by using the Paediatric Quality of Life Inventory (PedsQL 4.0 versions for children and parents). The study included 22 children with cerebral palsy spastic, classified in levels I, II, and III on the Gross Motor Function Classification System (GMFCS), with age group of 9.9 years old. Among those who have participated, seven of them were level I, eight of them were level II and seven of them were level III. All of the children and teenagers were rated by using check list ICF (mobility item), GMFM-88, 1-minute walk test and PedsQL 4.0 questionnaires for children and parents. It was observed a strong correlation between GMFM-88 with check list ICF (mobility item), but moderate correlation between GMFM-88 and 1-minute walk test (1MWT). It was also moderate the correlation between the walking test and the check list ICF (mobility item). The correlation between PedsQl 4.0 questionnaires for children and parents was weak, as well as the correlation of both with GMFM, ICF (mobility item) and the walking test. The lack of interrelation between physical function tests and quality of life, indicates that, regardless of the severity of the motor impairment and the difficulty with mobility, children and teenagers suffering of PC spastic, functional level I, II and III GMFCS and their parents have a varied opinion regarding the perception of well being and life satisfaction.

A Model of Lung Tumor Angiogenesis in a Biomimetic Poly(ethylene glycol)-based Hydrogel System

Tumor angiogenesis is critical to tumor growth and metastasis, yet much is unknown about the role vascular cells play in the tumor microenvironment. A major outstanding challenge associated with studying tumor angiogenesis is that existing preclinical models are limited in their recapitulation of in vivo cellular organization in 3D. This disparity highlights the need for better approaches to study the dynamic interplay of relevant cells and signaling molecules as they are organized in the tumor microenvironment. In this thesis, we combined 3D culture of lung adenocarcinoma cells with adjacent 3D microvascular cell culture in 2-layer cell-adhesive, proteolytically-degradable poly(ethylene glycol) (PEG)-based hydrogels to study tumor angiogenesis and the impacts of neovascularization on tumor cell behavior.

In initial studies, 344SQ cells, a highly metastatic, murine lung adenocarcinoma cell line, were characterized alone in 3D in PEG hydrogels. 344SQ cells formed spheroids in 3D culture and secreted proangiogenic growth factors into the conditioned media that significantly increased with exposure to transforming growth factor beta 1 (TGF-β1), a potent tumor progression-promoting factor. Vascular cells alone in hydrogels formed tubule networks with localized activated TGF-β1. To study cancer cell-vascular cell interactions, the engineered 2-layer tumor angiogenesis model with 344SQ and vascular cell layers was employed. Large, invasive 344SQ clusters developed at the interface between the layers, and were not evident further from the interface or in control hydrogels without vascular cells. A modified model with spatially restricted 344SQ and vascular cell layers confirmed that observed 344SQ cluster morphological changes required close proximity to vascular cells. Additionally, TGF-β1 inhibition blocked endothelial cell-driven 344SQ migration.

Two other lung adenocarcinoma cell lines were also explored in the tumor angiogenesis model: primary tumor-derived metastasis-incompetent, murine 393P cells and primary tumor-derived metastasis-capable human A549 cells. These lung cancer cells also formed spheroids in 3D culture and secreted proangiogenic growth factors into the conditioned media. Epithelial morphogenesis varied for the primary tumor-derived cell lines compared to 344SQ cells, with far less epithelial organization present in A549 spheroids. Additionally, 344SQ cells secreted the highest concentration of two of the three angiogenic growth factors assessed. This finding correlated to 344SQ exhibiting the most pronounced morphological response in the tumor angiogenesis model compared to the 393P and A549 cell lines.

Overall, this dissertation demonstrates the development of a novel 3D tumor angiogenesis model that was used to study vascular cell-cancer cell interactions in lung adenocarcinoma cell lines with varying metastatic capacities. Findings in this thesis have helped to elucidate the role of vascular cells in tumor progression and have identified differences in cancer cell behavior in vitro that correlate to metastatic capacity, thus highlighting the usefulness of this model platform for future discovery of novel tumor angiogenesis and tumor progression-promoting targets.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.

An Agent-based Classification Model

The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.