Phylogenetic relationships among nine scallop species (Bivalvia : Pectinidae) inferred from nucleotide sequences of one mitochondrial and three nuclear gene regions

Current knowledge of the evolutionary relationships among scallop species (Mollusca: Bivalvia: Pectinidae) in the Indo-Pacific region is rather scanty. To enhance the understanding of the relationships within this group, phylogenies of nine species of scallops with the majority from coastal regions of Thailand, were reconstructed by maximum parsimony, maximum likelihood, and Bayesian methods using sequences of the 16S rRNA of the mitochondrial genome, and a fragment containing the ITS1, 5.8S and ITS2 genes of the nuclear DNA. The trees that resulted from the three methods of analysis were topologically identical, however, gained different levels of support at some nodes. Nine species were clustered into two major clades, corresponding to two subfamilies (Pectininae and Chlamydinae) of the three currently recognized subfamilies within Pectinidae. Overall, the relationships reported herein are mostly in accordance with the previous molecular studies that used sequences of the mtDNA cytochrome oxidase subunit I, and the classification system based on microsculpture of shell features and morphological characteristics of juveniles. Levels of divergences were different among genes (i.e., the 5.8S gene showed the lowest levels of nucleotide divergence at all levels, whereas the 16S rRNA showed the highest level of variation within species, and ITS2 gene revealed the highest level of divergence at higher levels).

Extensible motif and aberrant nucleotide extraction in genomic sequences

The project focusses on the discovery of conserved DNA sequences in bacterial genomes and comparative analysis of bacterial genomes to elicit evolutionary trends. The outcomes have produced novel techniques for modelling motifs in DNA and the characterisation of evolutionary processes in medically significant bacterial pathogens.

A study on the accuracy of frequency measures and its impact on knowledge discovery in single sequences

In knowledge discovery in single sequences, different results could be discovered from the same sequence when different frequency measures are adopted. It is natural to raise such questions as (1) do these frequency measures reflect actual frequencies accurately? (2) what impacts do frequency measures have on discovered knowledge? (3) are discovered results accurate and reliable? and (4) which measures are appropriate for reflecting frequencies accurately? In this paper, taking three major factors (anti-monotonicity, maximum-frequency and window-width restriction) into account, we identify inaccuracies inherent in seven existing frequency measures, and investigate their impacts on the soundness and completeness of two kinds of knowledge, frequent episodes and episode rules, discovered from single sequences. In order to obtain more accurate frequencies and knowledge, we provide three recommendations for defining appropriate frequency measures. Following the recommendations, we introduce a more appropriate frequency measure. Empirical evaluation reveals the inaccuracies and verifies our findings. 

The Carboniferous and Permian sequences in north and south of the Longmucuo-Shuanghu suture zone in Tibet

The location of the Palaeo-tethys suture in Tibet has been in great dispute for past two decades. The Longmucuo-Shuanghu suture has long been considered as the Palaeo-tethys in Tibet. Restudy of the Carboniferous and Permian sequences in the north and south of this suture reveal that: (1) the Carboniferous and Permian sequence of the North Qiangtang Block is characterized by containing compound corals and intact fusulinids zones from Moscovian Fusulinella, Fusulina to Changhsingian Palaeofusulina zones; (2) the Early Permian of the South Qiangtang Block is

Fast mining of non-derivable episode rules in complex sequences

Researchers have been endeavoring to discover concise sets of episode rules instead of complete sets in sequences. Existing approaches, however, are not able to process complex sequences and can not guarantee the accuracy of resulting sets due to the violation of anti-monotonicity of the frequency metric. In some real applications, episode rules need to be extracted from complex sequences in which multiple items may appear in a time slot. This paper investigates the discovery of concise episode rules in complex sequences. We define a concise representation called non-derivable episode rules and formularize the mining problem. Adopting a novel anti-monotonic frequency metric, we then develop a fast approach to discover non-derivable episode rules in complex sequences. Experimental results demonstrate that the utility of the proposed approach substantially reduces the number of rules and achieves fast processing.

Subsequence frequency measurement and its impact on knowledge discovery in single sequences

Subsequence frequency measurement is a basic and essential problem in knowledge discovery in single sequences. Frequency based knowledge discovery in single sequences tends to be unreliable since different resulting sets may be obtained from a same sequence when different frequency metrics are adopted. In this chapter, we investigate subsequence frequency measurement and its impact on the reliability of knowledge discovery in single sequences. We analyse seven previous frequency metrics, identify their inherent inaccuracies, and explore their impacts on two kinds of knowledge discovered from single sequences, frequent episodes and episode rules. We further give three suggestions for frequency metrics and introduce a new frequency metric in order to improve the reliability. Empirical evaluation reveals the inaccuracies and verifies our findings.

Highly efficient serial episode discovery in streams and complex sequences

This research proposed a series of methodologies and algorithms for highly efficient serial episode discovery in streams and complex sequences, and applied the developed techniques to quantitative analysis of the effects of price promotions. This research has outputted nine ERA ranking AlB papers published in international journals and conferences.

Qualitative estimation of camera motion parameters from video sequences

We propose a simple technique for extracting camera motion parameters from a sequence of images. The method can estimate qualitatively camera pan, tilt, zoom, roll, and horizontal and vertical tracking. Unlike most other comparable techniques, the present method can distinguish pan from horizontal tracking, and tilt from vertical tracking. The technique can be applied to the automated indexing of video and film sequences.

Two-dimensional string notation for representing video sequences

Most current work on video indexing concentrates on queries which operate over high level semantic information which must be entirely composed and entered manually. We propose an indexing system which is based on spatial information about key objects in a scene. These key objects may be detected automatically, with manual supervision, and tracked through a sequence using one of a number of recently developed techniques. This representation is highly compact and allows rapid resolution of queries specified by iconic example. A number of systems have been produced which use 2D string notations to index digital image libraries. Just as 2D strings provide a compact and tractable indexing notation for digital pictures, a sequence of 2D strings might provide an index for a video or image sequence. To improve further upon this we reduce the representation to the 2D string pair representing the initial frame, and a sequence of edits to these strings. This takes advantage of the continuity between frames to further reduce the size of the notation. By representing video sequences using string edits, a notation has been developed which is compact, and allows querying on the spatial relationships of objects to be performed without rebuilding the majority of the scene. Calculating ranks of objects directly from the edit sequence allows matching with minimal calculation, thus greatly reducing search time. This paper presents the edit sequence notation and algorithms for evaluating queries over image sequences. A number of optimizations which represent a considerably saving in search time is demonstrated in the paper.

Generating comprehensible summaries of rushes sequences based on robust feature matching

This paper describes our first attempt at tackling a pilot task in Trecvid: video summarization of rushes data [3]. Our method is based on the tight clustering produced via SIFT matching. In this first attempt, we try to examine how our approach performs without complex implementation in terms of concept detection and excerpt assembly (i.e, no picture-in-picture, split screen and special transitions). Although we do not perform very well in terms of concept inclusion, we rank very well in terms of the summary being easy to understand and relevancy of included segments.

Finding the optimal temporal partitioning of video sequences

The existing techniques for shot partitioning either process each shot boundary independently or proceed sequentially. The sequential process assumes the last shot boundary is correctly detected and utilizes the shot length distribution to adapt the threshold for detecting the next boundary. These techniques are only locally optimal and suffer from the strong assumption about the correct detection of the last boundary. Addressing these fundamental issues, in this paper, we aim to find the global optimal shot partitioning by utilizing Bayesian principles to model the probability of a particular video partition being the shot partition. A computationally efficient algorithm based on Dynamic Programming is then formulated. The experimental results on a large movie set show that our algorithm performs consistently better than the best adaptive-thresholding technique commonly used for the task.