Biological values of acupuncture and Chinese herbal medicine : impact on the life science

Editorial paper

Identification of eusynstyelamide B as a potent cell cycle inhibitor following the generation and screening of an ascidian-derived extract library using a real time cell analyzer

Ascidians are marine invertebrates that have been a source of numerous cytotoxic compounds. Of the first six marine-derived drugs that made anticancer clinical trials, three originated from ascidian specimens. In order to identify new anti-neoplastic compounds, an ascidian extract library (143 samples) was generated and screened in MDA-MB-231 breast cancer cells using a real-time cell analyzer (RTCA). This resulted in 143 time-dependent cell response profiles (TCRP), which are read-outs of changes to the growth rate, morphology, and adhesive characteristics of the cell culture. Twenty-one extracts affected the TCRP of MDA-MB-231 cells and were further investigated regarding toxicity and specificity, as well as their effects on cell morphology and cell cycle. The results of these studies were used to prioritize extracts for bioassay-guided fractionation, which led to the isolation of the previously identified marine natural product, eusynstyelamide B (1). This bis-indole alkaloid was shown to display an IC50 of 5 μM in MDA-MB-231 cells. Moreover, 1 caused a strong cell cycle arrest in G2/M and induced apoptosis after 72 h treatment, making this molecule an attractive candidate for further mechanism of action studies.

Wound healing from the outback: novel wound healing therapeutics from native Australian plants

Introduction Chronic wounds are an area of major concern. The on-going and in-direct costs are substantial, reaching far beyond the costs of the hospitalization and associated care. As a result, pharmacological therapies have been developed to address treatment insufficiencies, however, the availability of drugs capable of promoting the wound repair process still remain limited. The wound healing properties of various herbal plants is well recognised amongst indigenous Australians. Hence, based on traditional accounts, we evaluated the wound healing potential of two Australian native plants. Methods Bioactive compounds were methanol extracted from dried plant leaves that were commercially sourced. Primary keratinocyte (Kc) and fibroblast (Fib) cells (denoted as Kc269, Kc274, Kc275, Kc276 and Fib274) obtained from surgical discarded tissue were cultured in 48-well plates and incubated (37⁰C, 5% CO2) overnight. The growth media was discarded and replaced with fresh growth media plus various concentrations (15.12 µg/mL, 31.25 µg/mL, 62.5 µg/mL, 125 µg/mL, 250 µg/mL and 500 µg/mL) of the plant extracts. Cellular responses were measured using the alamarBlue® assay and the CyQUANT® assay. Plant extracts in the aqueous phase were prepared by boiling whole leaves in water and taking aqueous phase samples at various (1, 2 , 5 minutes boiling) time points. Plant leaves were either added before the water was boiled (cold boiled) or after the water was boiled (hot boiled). The final concentrations of the aqueous plant extracts were 3.3 ng/mL (± 0.3 ng/mL) per sample. The antimicrobial properties of the plant extracts were tested using the well diffusion assay method against Staphylococcus aureus, Klebsiella pnuemoniae and methicillin resistant S. aureus and Bacillus cereus. Results Assay results from the almarBlue® and CYQUANT® assays indicated that extracts from both native plants at various time points (0, 24 and 48 hours) and concentrations (31.25 mg/mL, 62.5 mg/mL, and 125 mg/mL) were significantly higher (n=3, p=0.03 for Kc269, p=0.04 for Kc274, p=0.02 for Fib274, p=0.04 for Kc275 and p=0.001 for Kc276) compared with the untreated controls. Neither plant extract demonstrated cytotoxic effects. Significant antimicrobial activity against methicillin resistant Staphylococcus aureus (p=0.0009 for hot boiled plant A, n=2, p=0.034 for cold boiled plant A, n=2) K. pnuemoniae (p=0.0009 for hot boiled plant A, n=2, p=0.002 for cold boiled plant A, n=2) and B. cereus (p=0.0009 for hot boiled plant A, n=2, p=0.003 for cold boiled plant A, n=2) was observed at concentrations of 3.2 ng/mL for plant A and 3.4 ng/mL for plant B. Conclusion Both native plants contain bioactive compounds that increase cellular metabolic rates and total nucleic acid content. Neither plant was shown to be cytotoxic. Furthermore, both exhibited significant antimicrobial activity.

Investigating the potential of Shikonin, a Chinese herbal medicine, as a novel scar remediation therapy

This project investigates for the first time the biological mechanisms underlying the anecdotal use of Shikonin, an active component extracted from the Chinese herbal medicine "Zi Cao", as a treatment for hypertrophic scars. Compelling molecular and cellular evidence was generated supporting the therapeutic value of Shikonin as a scar treatment, suggesting that further development of this agent is warranted.

Extract from plants

An extract of organic material from a species selected from the tribe Nicotianeae that comprises a mixture of C26 to C33 alkanes at a purity of at least 90% by volume of total extract.

Characterisation of indoor air organic pollutants in Brisbane

The occurrence and levels of airborne polycyclic aromatic hydrocarbons and volatile organic compounds in selected non-industrial environments in Brisbane have been investigated as part of an integrated indoor air quality assessment program. The most abundant and most frequently encountered compounds include, nonanal, decanal, texanol, phenol, 2-ethyl-1-hexanol, ethanal, naphthalene, 2,6-tert-butyl-4-methyl-phenol (BHT), salicylaldehyde, toluene, hexanal, benzaldehyde, styrene, ethyl benzene, o-, m- and pxylenes, benzene, n-butanol, 1,2-propandiol, and n-butylacetate. Many of the 64 compounds usually included in the European Collaborative Action method of TVOC analysis were below detection limits in the samples analysed. In order to extract maximum amount of information from the data collected, multivariate data projection methods have been employed. The implications of the information extracted on source identification and exposure control are discussed.

Peer to peer English/Chinese cross-language information retrieval

Peer to peer systems have been widely used in the internet. However, most of the peer to peer information systems are still missing some of the important features, for example cross-language IR (Information Retrieval) and collection selection / fusion features. Cross-language IR is the state-of-art research area in IR research community. It has not been used in any real world IR systems yet. Cross-language IR has the ability to issue a query in one language and receive documents in other languages. In typical peer to peer environment, users are from multiple countries. Their collections are definitely in multiple languages. Cross-language IR can help users to find documents more easily. E.g. many Chinese researchers will search research papers in both Chinese and English. With Cross-language IR, they can do one query in Chinese and get documents in two languages. The Out Of Vocabulary (OOV) problem is one of the key research areas in crosslanguage information retrieval. In recent years, web mining was shown to be one of the effective approaches to solving this problem. However, how to extract Multiword Lexical Units (MLUs) from the web content and how to select the correct translations from the extracted candidate MLUs are still two difficult problems in web mining based automated translation approaches. Discovering resource descriptions and merging results obtained from remote search engines are two key issues in distributed information retrieval studies. In uncooperative environments, query-based sampling and normalized-score based merging strategies are well-known approaches to solve such problems. However, such approaches only consider the content of the remote database but do not consider the retrieval performance of the remote search engine. This thesis presents research on building a peer to peer IR system with crosslanguage IR and advance collection profiling technique for fusion features. Particularly, this thesis first presents a new Chinese term measurement and new Chinese MLU extraction process that works well on small corpora. An approach to selection of MLUs in a more accurate manner is also presented. After that, this thesis proposes a collection profiling strategy which can discover not only collection content but also retrieval performance of the remote search engine. Based on collection profiling, a web-based query classification method and two collection fusion approaches are developed and presented in this thesis. Our experiments show that the proposed strategies are effective in merging results in uncooperative peer to peer environments. Here, an uncooperative environment is defined as each peer in the system is autonomous. Peer like to share documents but they do not share collection statistics. This environment is a typical peer to peer IR environment. Finally, all those approaches are grouped together to build up a secure peer to peer multilingual IR system that cooperates through X.509 and email system.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

High-level concept annotation using ontology and probabilistic inference

Image annotation is a significant step towards semantic based image retrieval. Ontology is a popular approach for semantic representation and has been intensively studied for multimedia analysis. However, relations among concepts are seldom used to extract higher-level semantics. Moreover, the ontology inference is often crisp. This paper aims to enable sophisticated semantic querying of images, and thus contributes to 1) an ontology framework to contain both visual and contextual knowledge, and 2) a probabilistic inference approach to reason the high-level concepts based on different sources of information. The experiment on a natural scene database from LabelMe database shows encouraging results.

Characterising anomalous events using change point correlation on unsolicited network traffic

Monitoring unused or dark IP addresses offers opportunities to extract useful information about both on-going and new attack patterns. In recent years, different techniques have been used to analyze such traffic including sequential analysis where a change in traffic behavior, for example change in mean, is used as an indication of malicious activity. Change points themselves say little about detected change; further data processing is necessary for the extraction of useful information and to identify the exact cause of the detected change which is limited due to the size and nature of observed traffic. In this paper, we address the problem of analyzing a large volume of such traffic by correlating change points identified in different traffic parameters. The significance of the proposed technique is two-fold. Firstly, automatic extraction of information related to change points by correlating change points detected across multiple traffic parameters. Secondly, validation of the detected change point by the simultaneous presence of another change point in a different parameter. Using a real network trace collected from unused IP addresses, we demonstrate that the proposed technique enables us to not only validate the change point but also extract useful information about the causes of change points.

St. John’s wort and Kava in treating major depressive disorder with comorbid anxiety : a randomised double-blind placebo-controlled pilot trial

Objective - We report the first randomised controlled trial (RCT) using a combination of St. John’s wort (SJW) and Kava for the treatment of major depressive disorder (MDD) with comorbid anxiety. Methods - Twenty-eight adults with MDD and co-occurring anxiety were recruited for a double-blind RCT. After a placebo run-in of 2 weeks, the trial had a crossover design testing SJW and Kava against placebo over two controlled phases, each of 4 weeks. The primary analyses used intention-to-treat and completer analyses. Results - On both intention-to-treat ( p¼0.047) and completer analyses ( p¼0.003), SJW and Kava gave a significantly greater reduction in self-reported depression on the Beck Depression Inventory (BDI-II) over placebo in the first controlled phase. However, in the crossover phase, a replication of those effects in the delayed medication group did not occur. Nor were there significant effects on anxiety or quality of life. Conclusion - There was some evidence of antidepressant effects using SJW and Kava in a small sample with comorbid anxiety. Possible explanations for the absence of anxiolysis may include a potential interaction with SJW, the presence of depression, or an inadequate dose of Kava.

Student resources for the use of supervisors

The materials presented here are intended to: a) accompany the document Supervisor Resource and b) provide technology supervisors with materials that may be readily shared with students. These resources are not designed to be distributed to students without contextualization, they are intended for use in workshops or in discussions between supervisors and students. As authors, we anticipate that supervisors or workshop facilitators are most likely to extract individual resources of interest for particular occasions. The materials have been developed from conversations with supervisors from the technology disciplines.

Crash Risk Assessment with Cooperative Systems

Crash risk is the statistical probability of a crash. Its assessment can be performed through ex post statistical analysis or in real-time with on-vehicle systems. These systems can be cooperative. Cooperative Vehicle-Infrastructure Systems (CVIS) are a developing research avenue in the automotive industry worldwide. This paper provides a survey of existing CVIS systems and methods to assess crash risk with them. It describes the advantages of cooperative systems versus non-cooperative systems. A sample of cooperative crash risk assessment systems is analysed to extract vulnerabilities according to three criteria: market penetration, over-reliance on GPS and broadcasting issues. It shows that cooperative risk assessment systems are still in their infancy and requires further development to provide their full benefits to road users.