The bipolar depression rating scale (BDRS) : its development, validation and utility

Objectives: Unipolar and bipolar depression differ neurobiologically and in clinical presentation. Existing depression rating instruments, used in bipolar depression, fail to capture the necessary phenomenological nuances, as they are based on and skewed towards the characteristics of unipolar depression. Both clinically and in research there is a growing need for a new observer-rated scale that is specifically designed to assess bipolar depression.

Methods: An instrument reflecting the characteristics of bipolar depression was drafted by the authors, and administered to 122 participants aged 18–65 (44 males and 78 females) with a diagnosis of DSM-IV bipolar disorder, who were currently experiencing symptoms of depression. The Bipolar Depression Rating Scale (BDRS) was administered together with the Hamilton Depression Rating Scale (HAM-D), Montgomery Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS).

Results: The BDRS has strong internal consistency (Cronbach's alpha = 0.917), and robust correlation coefficients with the MADRS (r = 0.906) and HAM-D (r = 0.744), and the mixed subscale correlated with the YMRS (r = 0.757). Exploratory factor analysis showed a three-factor solution gave the best account of the data. These factors corresponded to depression (somatic), depression (psychological) and mixed symptom clusters.

Conclusions: This study provides evidence for the validity of the BDRS for the measurement of depression in bipolar disorder. These results suggest good internal validity, provisional evidence of inter-rater reliability and strong correlations with other depression rating scales.

The validity and internal structure of the bipolar depression rating scale (BDRS): data from a clinical trial of N-acetylcysteine as adjunctive therapy in bipolar disorder

Background: The phenomenology of unipolar and bipolar disorders differ in a number of ways, such as the presence of mixed states and atypical features. Conventional depression rating instruments are designed to capture the characteristics of unipolar depression and have limitations in capturing the breadth of bipolar disorder.

Method: The Bipolar Depression Rating Scale (BDRS) was administered together with the Montgomery Asberg Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) in a double-blind randomised placebo-controlled clinical trial of N-acetyl cysteine for bipolar disorder (N = 75).

Results: A factor analysis showed a two-factor solution: depression and mixed symptom clusters. The BDRS has strong internal consistency (Cronbach's alpha = 0.917), the depression cluster showed robust correlation with the MADRS (r = 0.865) and the mixed subscale correlated with the YMRS (r = 0.750).

Conclusion: The BDRS has good internal validity and inter-rater reliability and is sensitive to change in the context of a clinical trial.

Automatic parallelisation and execution of applications on clusters

Parallel execution is a very efficient means of processing vast amounts of data in a small amount of time. Creating parallel applications has never been easy, and requires much knowledge of the task and the execution environment used to execute parallel processes. The process of creating parallel applications can be made easier through using a compiler that automatically parallelises a supplied application. Executing the parallel application is also simplified when a well designed execution environment is used. Such an execution environment provides very powerful operations to the programmer transparently. Combining both a parallelising compiler and execution environment and providing a fully automated parallelisation and execution tool is the aim of this research. The advantage of using such a fully automated tool is that the user does not need to provide any additional input to gain the benefits of parallel execution. This report shows the tool and how it transparently supports the programmer creating parallel applications and supports their execution.

Preemptive job scheduling policy for distributively-owned workstation clusters

Cluster computing has come to prominence as a cost-effective parallel processing tool for solving many complex computational problems. In this paper, we propose a new timesharing opportunistic scheduling policy to support remote batch job executions over networked clusters to be used in conjunction with the Condor Up-Down scheduling algorithm. We show that timesharing approaches can be used in an opportunistic setting to improve both mean job slowdowns and mean response times with little or no throughput reduction. We also show that the proposed algorithm achieves significant improvement in job response time and slowdown as compared to exiting approaches and some recently proposed new approaches.

A comparative study at the logical level of centralised and distributed recovery in clusters

Cluster systems are becoming more prevalent in today’s computer society and users are beginning to request that these systems be reliable. Currently, most clusters have been designed to provide high performance at the cost of providing little to no reliability. To combat this, this report looks at how a recovery facility, based on either a centralised or distributed approach could be implemented into a cluster that is supported by a check pointing facility. This recovery facility can then recover failed user processes by using checkpoints of the processes that have been taken during failure free execution.

Execution environments and benchmarks for the study of applications' scheduling on clusters

In this paper, we have demonstrated how the existing programming environments, tools and middleware could be used for the study of execution performance of parallel and sequential applications on a non-dedicated cluster. A set of parallel and sequential benchmark applications selected for and used in the experiments were characterized, and experiment requirements shown. 

Building autonomic clusters : a response to IBM’s autonomic computing challenge

IBM has announced Autonomic Computing as one of its Grand Challenges. A list of characteristics was generated to describe an autonomic computing system. Many researchers and research group have responded positively to the challenge by initiating research around one or two of the characteristics identified by IBM as the requirements for Autonomic Computing. We have addressed the IBMrsquos request to contribute to the challenge by building an autonomic operating system that possesses nearly all IBM characteristics. This paper contains our response to the IBMrsquos Autonomic Computing Challenge.

Toward an operating system that supports parallel processing on nondedicated clusters

Present operating systems are not built to support parallel computing on clusters - they do not provide services to manage parallelism, i.e., to manage parallel processes and cluster resources. They do not provide support for both programming paradigms, Message Passing (MP) or Distributed Shared Memory (DSM). Due to poor operating systems, users must deal with computers of a cluster rather than to see this cluster as a single powerful computer. There is a need for cluster operating systems. We claim that it is possible to develop a cluster operating system that is able to efficiently manage parallelism, support MP and DSM and offer transparency. To substantiate this claim the first version of a cluster operating system managing parallelism and offering transparency, called GENESIS, has been developed.

Toward highly available, self-healing, adaptable, grid-like and user friendly nondedicated clusters

The human body was used to illustrate an Autonomic Computing system that possesses self-knowledge, self-configuration, self-optimization, self-healing, and self-protection, knowledge of its environment and user friendliness properties. Autonomic Computing was identified by IBM as one of the Grand Challenges. Many researchers and research groups have responded positively to the challenge by initiating research around one or two of the characteristics
identified by IBM as the requirements for Autonomic Computing. One of the areas that could benefit from the comprehensive approach created by the Autonomic Computing vision is parallel processing on nondedicated clusters. This paper shows a general design of services and initial implementation of a system that moves parallel processing on clusters to the computing mainstream using the Autonomic Computing vision.

Building autonomic clusters: a response to IBM's autonomic computing challenge

IBM has announced Autonomic Computing as one of its Grand Challenges. A list of characteristics was generated to describe an autonomic computing system. Many researchers and research group have responded positively to the challenge by initiating research around one or two of the characteristics identified by IBM as the requirements for Autonomic Computing. We have addressed the IBMs request to contribute to the challenge by building an autonomic operating system that possesses nearly all IBM characteristics. This paper contains our response to the IBMs Autonomic Computing Challenge.

Knowledge management for SME-based regional clusters

This paper considers the need for knowledge management (KM) in regional clusters comprising many small and medium enterprises (SMEs) and the appropriate KM techniques for this form of economic organisation. Information and communication technologies offer a range of tools to help such clusters develop into electronically-linked eClusters, making KM possible on a scale not previously possible. Most KM techniques have been developed by large organisations and their relevance to SME-based clusters has received little attention. Based on our analysis of the literature, we conclude that KM approaches based on personalised rather than codified information are the most promising model for regionally-based eClusters and that Communities of Practice arising from open forms of internet collaboration are most likely to be successful in this environment. Future research will identify key issues and appropriate techniques for supporting regional clusters with electronic systems for KM.