Taxonomy of Australian Funnel-web spiders using rp-HPLC/ESI-MS profiling techniques

The complex nature of venom from spider species offers a unique natural source of potential pharmacological tools and therapeutic leads. The increased interest in spider venom molecules requires reproducible and precise identification methods. The current taxonomy of the Australian Funnel-web spiders is incomplete, and therefore, accurate identification of these spiders is difficult. Here, we present a study of venom from numerous morphologically similar specimens of the Hadronyche infensa species group collected from a variety of geographic locations in southeast Queensland. Analysis of the crude venoms using online reversed-phase high performance liquid chromatography/electrospray ionisation mass spectrometry (rp-HPLC/ESI-MS) revealed that the venom profiles provide a useful means of specimen identification, from the species level to species variants. Tables defining the descriptor molecules for each group of specimens were constructed and provided a quick reference of the relationship between one specimen and another. The study revealed that the morphologically similar specimens from the southeast Queensland region are a number of different species/species variants. Furthermore, the study supports aspects of the current taxonomy with respect to the H. infensa species group. Analysis of Australian Funnel-web spider venom by rp-HPLC/ESI-MS provides a rapid and accurate method of species/species variant identification. (c) 2006 Elsevier Ltd. All rights reserved.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Mutation analysis to verify feature matrices for isolating errors in simulation models

Software simulation models are computer programs that need to be verified and debugged like any other software. In previous work, a method for error isolation in simulation models has been proposed. The method relies on a set of feature matrices that can be used to determine which part of the model implementation is responsible for deviations in the output of the model. Currrently these feature matrices have to be generated by hand from the model implementation, which is a tedious and error-prone task. In this paper, a method based on mutation analysis, as well as prototype tool support for the verification of the manually generated feature matrices is presented. The application of the method and tool to a model for wastewater treatment shows that the feature matrices can be verified effectively using a minimal number of mutants.

Critical feature analysis of a radiotherapy machine

The software implementation of the emergency shutdown feature in a major radiotherapy system was analyzed, using a directed form of code review based on module dependences. Dependences between modules are labelled by particular assumptions; this allows one to trace through the code, and identify those fragments responsible for critical features. An `assumption tree' is constructed in parallel, showing the assumptions which each module makes about others. The root of the assumption tree is the critical feature of interest, and its leaves represent assumptions which, if not valid, might cause the critical feature to fail. The analysis revealed some unexpected assumptions that motivated improvements to the code.