Evaluation of a brief child-focused group-based intervention for anxiety-disordered children

This paper presents a pilot study of a brief, group-based, cognitive-behavioural intervention for anxiety-disordered children. Five children (aged 7 to 13 years) diagnosed with a clinically significant anxiety disorder were treated with a recently developed 6-session, child-focused, cognitive-behavioural intervention that was evaluated using multiple measures (including structured diagnostic interview, self-report questionnaires and behaviour rating scales completed by parents) over four follow-up occasions (posttreatment, 3-month follow-up, 6-month follow-up and 12-month follow-up). This trial aimed to (a) evaluate the conclusion suggested by the research of Cobham, Dadds, and Spence (1998) that anxious children with non-anxious parents require a child-focused intervention only in order to demonstrate sustained clinical gains; and (b) to evaluate a new and more cost-effective child-focused cognitive-behavioural intervention. Unfortunately, the return rate of the questionnaires was poor, rendering this data source of questionable value. However, diagnostic interviews (traditionally the gold standard in terms of outcome in this research area) were completed for all children at all follow-up points. Changes in diagnostic status indicated that meaningful treatment-related gains had been achieved and were maintained over the full follow-up period. The results would thus seem to support the principle of participant-intervention matching proposed by Cobham et al. (1998), as well as the utility of the more brief intervention evaluated.

Effects of patient-focused care on seclusion in a psychiatric intensive care unit

The objective of the study was to evaluate whether the introduction of patient-focused nursing care affected the number of seclusions and the length of time patients spent in seclusion, in an acute psychiatric unit. The study used a pre-intervention–post-intervention design and was conducted in an eight-bed locked unit within a large regional general hospital in Queensland, Australia. The medical records of all people who were secluded as part of their management while in hospital, during two 6-month periods, were retrospectively reviewed. Changes to the ways in which nurses conducted their daily activities were implemented during the time between the data collection periods. There were no differences between the groups with respect to the number of times a patient was secluded. However, following implementation of patient-focused care, there was a reduction in the length of time for which patients were secluded. The only change in medication administration was that post-implementation, Haloperidol was used in fewer seclusion episodes. The findings indicate that changes to nursing practice may result in closer monitoring of patients and a reduction in the time patients spend secluded in acute inpatient psychiatric settings.

Focused beam reflectance technique for in situ particle sizing in wastewater treatment settling tanks

Due to the complexities involved with measuring activated sludge floc size distributions, this parameter has largely been ignored by wastewater researchers and practitioners. One of the major reasons has been that instruments able to measure particle size distributions were complex, expensive and only provided off-line measurements. The Focused Beam Reflectance Method (FBRM) is one of the rare techniques able to measure the particle size distribution in situ. This paper introduces the technique for monitoring wastewater treatment systems and compares its performance with other sizing techniques. The issue of the optimal focal point is discussed, and similar conclusions as found in the literature for other particulate systems are drawn. The study also demonstrates the capabilities of the FBRM in evaluating the performance of settling tanks. Interestingly, the floc size distributions did not vary with position inside the settling tank flocculator. This was an unexpected finding, and seriously questioned the need for a flocculator in the settling tank. It is conjectured that the invariable size distributions were caused by the unique combination of high solids concentration, low shear and zeolite dosing. (C) 2004 Society of Chemical Industry.

Countering cooperative effects in protease inhibitors using constrained b-strand-mimicking templates in focused combinatorial libraries

A major problem in de novo design of enzyme inhibitors is the unpredictability of the induced fit, with the shape of both ligand and enzyme changing cooperatively and unpredictably in response to subtle structural changes within a ligand. We have investigated the possibility of dampening the induced fit by using a constrained template as a replacement for adjoining segments of a ligand. The template preorganizes the ligand structure, thereby organizing the local enzyme environment. To test this approach, we used templates consisting of constrained cyclic tripeptides, formed through side chain to main chain linkages, as structural mimics of the protease-bound extended beta-strand conformation of three adjoining amino acid residues at the N- or C-terminal sides of the scissile bond of substrates. The macrocyclic templates were derivatized to a range of 30 structurally diverse molecules via focused combinatorial variation of nonpeptidic appendages incorporating a hydroxyethylamine transition-state isostere. Most compounds in the library were potent inhibitors of the test protease (HIV-1 protease). Comparison of crystal structures for five protease-inhibitor complexes containing an N-terminal macrocycle and three protease-inhibitor complexes containing a C-terminal macrocycle establishes that the macrocycles fix their surrounding enzyme environment, thereby permitting independent variation of acyclic inhibitor components with only local disturbances to the protease. In this way, the location in the protease of various acyclic fragments on either side of the macrocyclic template can be accurately predicted. This type of templating strategy minimizes the problem of induced fit, reducing unpredictable cooperative effects in one inhibitor region caused by changes to adjacent enzyme-inhibitor interactions. This idea might be exploited in template-based approaches to inhibitors of other proteases, where a beta-strand mimetic is also required for recognition, and also other protein-binding ligands where different templates may be more appropriate.

Focused, eight-element transceive phased array coil for parallel magnetic resonance imaging of the chest-theoretical considerations

A new transceive system for chest imaging for MRI applications is presented. A focused, eight-element transceive torso phased array coil is designed to investigate transmitting a focused radiofrequency field deep within the torso and to enhance signal homogeneity in the heart region. The system is used in conjunction with the SENSE reconstruction technique to enable focused parallel imaging. A hybrid finite-difference-time-domain/method-of-moments method is used to accurately predict the radiofrequency behavior inside the human torso. The simulation results reported herein demonstrate the feasibility of the design concept, which shows that radiofrequency field focusing with SENSE reconstruction is theoretically achievable. (c) 2005 Wiley-Liss, Inc.

Hierarchical indexing structure for efficient similarity search in video retrieval

With the rapid increase in both centralized video archives and distributed WWW video resources, content-based video retrieval is gaining its importance. To support such applications efficiently, content-based video indexing must be addressed. Typically, each video is represented by a sequence of frames. Due to the high dimensionality of frame representation and the large number of frames, video indexing introduces an additional degree of complexity. In this paper, we address the problem of content-based video indexing and propose an efficient solution, called the Ordered VA-File (OVA-File) based on the VA-file. OVA-File is a hierarchical structure and has two novel features: 1) partitioning the whole file into slices such that only a small number of slices are accessed and checked during k Nearest Neighbor (kNN) search and 2) efficient handling of insertions of new vectors into the OVA-File, such that the average distance between the new vectors and those approximations near that position is minimized. To facilitate a search, we present an efficient approximate kNN algorithm named Ordered VA-LOW (OVA-LOW) based on the proposed OVA-File. OVA-LOW first chooses possible OVA-Slices by ranking the distances between their corresponding centers and the query vector, and then visits all approximations in the selected OVA-Slices to work out approximate kNN. The number of possible OVA-Slices is controlled by a user-defined parameter delta. By adjusting delta, OVA-LOW provides a trade-off between the query cost and the result quality. Query by video clip consisting of multiple frames is also discussed. Extensive experimental studies using real video data sets were conducted and the results showed that our methods can yield a significant speed-up over an existing VA-file-based method and iDistance with high query result quality. Furthermore, by incorporating temporal correlation of video content, our methods achieved much more efficient performance.

ICICLE: A semantic-based retrieval system for WWW images

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

Automatic texture segmentation for texture-based image retrieval

Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose. This paper describes an automatic texture segmentation algorithm based on a set of features derived from wavelet domain, which are effective in texture description for retrieval purpose. Simulation results show that the proposed algorithm can efficiently capture the textured regions in arbitrary images, with the features of each region extracted as well. The features of each textured region can be directly used to index image database with applications as texture-based image retrieval.

Extraction of texture features from arbitrary-shaped regions for image retrieval

Lots of work has been done in texture feature extraction for rectangular images, but not as much attention has been paid to the arbitrary-shaped regions available in region-based image retrieval (RBIR) systems. In This work, we present a texture feature extraction algorithm, based on projection onto convex sets (POCS) theory. POCS iteratively concentrates more and more energy into the selected coefficients from which texture features of an arbitrary-shaped region can be extracted. Experimental results demonstrate the effectiveness of the proposed algorithm for image retrieval purposes.

Ecoregion classification using a bayesian approach and centre-focused clusters

Ecological regions are increasingly used as a spatial unit for planning and environmental management. It is important to define these regions in a scientifically defensible way to justify any decisions made on the basis that they are representative of broad environmental assets. The paper describes a methodology and tool to identify cohesive bioregions. The methodology applies an elicitation process to obtain geographical descriptions for bioregions, each of these is transformed into a Normal density estimate on environmental variables within that region. This prior information is balanced with data classification of environmental datasets using a Bayesian statistical modelling approach to objectively map ecological regions. The method is called model-based clustering as it fits a Normal mixture model to the clusters associated with regions, and it addresses issues of uncertainty in environmental datasets due to overlapping clusters.

Document re-ranking by generality in bio-medical information retrieval

Document ranking is an important process in information retrieval (IR). It presents retrieved documents in an order of their estimated degrees of relevance to query. Traditional document ranking methods are mostly based on the similarity computations between documents and query. In this paper we argue that the similarity-based document ranking is insufficient in some cases. There are two reasons. Firstly it is about the increased information variety. There are far too many different types documents available now for user to search. The second is about the users variety. In many cases user may want to retrieve documents that are not only similar but also general or broad regarding a certain topic. This is particularly the case in some domains such as bio-medical IR. In this paper we propose a novel approach to re-rank the retrieved documents by incorporating the similarity with their generality. By an ontology-based analysis on the semantic cohesion of text, document generality can be quantified. The retrieved documents are then re-ranked by their combined scores of similarity and the closeness of documents’ generality to the query’s. Our experiments have shown an encouraging performance on a large bio-medical document collection, OHSUMED, containing 348,566 medical journal references and 101 test queries.