Taxonomy and biology of two new species of gall midge (Diptera: Cecidomyiidae) infesting Sarcocornia quinqueflora (Chenopodiaceae) in Australian salt marshes

Two new species of gall midge associated with two distinct galls on the succulent creeping shrub Sarcocornia quinqueflora are described from salt marshes in south-eastern Australia. The infestations caused by the new species hinder the growth of S. quinqueflora, the seeds of which are the major food of the critically endangered orange-bellied parrot Neophema chrysogaster. Asphondylia floriformis sp. n. Veenstra-Quah & Kolesik transforms leaf segments into flower-like galls, whereas Asphondylia sarcocorniae sp. n. Veenstra-Quah & Kolesik produces simple swellings on branches. Both galls have fungal mycelium growing in the apoplast of the gall tissue and lining the inner surface of the larval chamber where it is presumably grazed by the larva. Descriptions of the larvae, pupae, males, females and the geographical distribution of the two gall midges in south-eastern Australia are given.

An evaluation of contemporary hidden Markov model genefinders with a predicted exon taxonomy

We present an independent evaluation of six recent hidden Markov model (HMM) genefinders. Each was tested on the new dataset (FSH298), the results of which showed no dramatic improvement over the genefinders tested five years ago. In addition, we introduce a comprehensive taxonomy of predicted exons and classify each resulting exon accordingly. These results are useful in measuring (with finer granularity) the effects of changes in a genefinder. We present an analysis of these results and identify four patterns of inaccuracy common in all HMM-based results.

The right tool for the job? Use of the multiple intelligences and Bloom's taxonomy grid

This article explores the tacit understanding of teachers in the field of gifted educational practices after their participation in gifted education professional development. The data for this article are drawn from a single-case qualitative study where semi-structured interviews were held with teachers, administrators and support staff in a metropolitan Victorian primary school. The findings lead to two main arguments: first, that some teachers preserved their deeply entrenched beliefs and assumptions about the gifted, the talented and intelligence[s]; and second, that teachers, without critical examination, eagerly adopted and adapted Gardner's Multiple Intelligences theory, overlaid with Bloom's Revised Taxonomy of Thinking as a means for addressing individual differences in the classroom. The article argues that teachers welcomed the Gardner/Bloom matrix for its 'tick-the-box' simplicity, with little insight into the theoretical models. Whilst the matrix had an immediate value in the mixed ability classrooms, in the long term it did not support the learning needs of gifted students. [Author abstract]

Dynamic feature selection for spam filtering using support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to differentiate spam from legitimate email. Much work have been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In this paper, architecture of spam filtering has been proposed based on support vector machine (SVM,) which will get better accuracy by reducing FP problems. In this architecture an innovative technique for feature selection called dynamic feature selection (DFS) has been proposed which is enhanced the overall performance of the architecture with reduction of FP problems. The experimental result shows that the proposed technique gives better performance compare to similar existing techniques.

Taxonomy and survey of location management systems

In wireless mobile computing, location management is introduced whenever users move from one place to another. In order to track a mobile user, the system must store information about their current location and report new locations to a home base station. Numerous techniques have been proposed to optimally manage the location of mobile hosts in mobile networks. This paper attempts to present a more structured and comprehensive analysis of the current location management techniques architectures and their technology enablers. We discuss some of the principal issues involved in location management and present a taxonomy and survey of location management strategies that have been proposed in the literature over the years for mobile computing systems.

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.

Clustering massive high dimensional data with dynamic feature maps

This paper presents an algorithm based on the Growing Self Organizing Map (GSOM) called the High Dimensional Growing Self Organizing Map with Randomness (HDGSOMr) that can cluster massive high dimensional data efficiently. The original GSOM algorithm is altered to accommodate for the issues related to massive high dimensional data. These modifications are presented in detail with experimental results of a massive real-world dataset.

The application of rough set and Kohonen network to feature selection for object extraction

Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The paper describes an application of rough sets method to feature selection and reduction in texture images recognition. The proposed methods include continuous data discretization based on Kohonen neural network and maximum covariance, and rough set algorithms for feature selection and reduction. The experiments on trees extraction from aerial images show that the methods presented in this paper are practical and effective.

Automatic pattern-taxonomy extraction for web mining

In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.

POEM, A 3-dimensional exon taxonomy and patterns in untranslated exons

Background: The existence of exons and introns has been known for thirty years. Despite this knowledge, there is a lack of formal research into the categorization of exons. Exon taxonomies used by researchers tend to be selected ad hoc or based on an information poor de-facto standard. Exons have been shown to have specific properties and functions based on among other things their location and order. These factors should play a role in the naming to increase specificity about which exon type(s) are in question.

Results: POEM (Protein Oriented Exon Monikers) is a new taxonomy focused on protein proximal exons. It integrates three dimensions of information (Global Position, Regional Position and Region), thus its exon categories are based on known statistical exon features. POEM is applied to two congruent untranslated exon datasets resulting in the following statistical properties. Using the POEM taxonomy previous wide ranging estimates of initial 5' untranslated region exons are resolved. According to our datasets, 29–36% of genes have wholly untranslated first exons. Untranslated exon containing sequences are shown to have consistently up to 6 times more 5' untranslated exons than 3' untranslated exons. Finally, three exon patterns are determined which account for 70% of untranslated exon genes.

Conclusion: We describe a thorough three-dimensional exon taxonomy called POEM, which is biologically and statistically relevant. No previous taxonomy provides such fine grained information and yet still includes all valid information dimensions. The use of POEM will improve the accuracy of genefinder comparisons and analysis by means of a common taxonomy. It will also facilitate unambiguous communication due to its fine granularity

Feature-based image fusion quality metrics

Image fusion quality metrics have evolved from image processing quality metrics. They measure the quality of fused images by estimating how much localized information has been transferred from the source images into the fused image. However, this technique assumes that it is actually possible to fuse two images into one without any loss. In practice, some features must be sacrificed and relaxed in both source images. Relaxed features might be very important, like edges, gradients and texture elements. The importance of a certain feature is application dependant. This paper presents a new method for image fusion quality assessment. It depends on estimating how much valuable information has not been transferred.

What do general practitioners think depression is? A taxonomy of distress and depression for general practice

Objective:
To create a taxonomy of distress and depression for use in primary care, that mirrors the thinking and practice of experienced general practitioners.

Design:
Qualitative study, using an ethnomethodological approach, with observation of videotaped routine GP–patient consultations and in-depth interviews with GPs.

Setting and participants:
The study was conducted in metropolitan Melbourne in 2005. Fourteen GPs conducted 36 patient consultations where depression was a focus; nine GPs participated in in-depth interviews to elicit details of how they recognised and diagnosed depression in their patients.

Results:
GPs consider distress and depression in three steps. In the first step, a change in a group of symptoms and signs is observed (eg, facial expression, loss of drive). The second step categorises the syndrome according to whether or not there is an identifiable environmental cause (reactive or “endogenous”), with the final step categorising the reactive syndromes according to their most prominent symptoms: either anxiety and worry, or helplessness and hopelessness. The resulting taxonomy includes: endogenous depression (a chronic and perhaps characterological depression characterised by a lack of interest and motivation); anxious depressive reaction (stress or worry); and hopeless depressive reaction (demoralisation).

Conclusion:
This simple and parsimonious taxonomy has validity based on its derivation from within the primary care setting.