Neil A. Ernst

Alternative Splicing, Methylation State, and Expression Profile of Tropomyosin-Related Kinase B in the Frontal Cortex of Suicide Completers

CONTEXT Although most of the effort to understand the neurobiology of depressive states and suicide has focused on neuronal processes, recent studies suggest that astroglial dysfunction may play an important role. A truncated variant of the tropomyosin-related kinase B (TrkB.T1) is expressed in astrocytes, and brain-derived neurotrophic factor-TrkB signaling has been linked to mood disorders. OBJECTIVE To test the hypothesis that TrkB.T1 expression is downregulated in suicide completers and that this downregulation is mediated by an epigenetic process. DESIGN Postmortem case-control study. Patients, Setting, and MAIN OUTCOME MEASURES Thirty-nine French Canadian men underwent screening at the Douglas Hospital Research Institute using the HG-U133 plus 2 microarray chip. Nine frontal cortical regions and the cerebellum were assessed using a microarray screening approach for extreme expression differences across subjects and a conventional screening approach. Results were validated by quantitative polymerase chain reaction and Western blot analyses. Animal experiments were performed to control for drug and alcohol effects. Genetic and epigenetic studies were performed by means of direct sequencing and bisulfite mapping. RESULTS We found that 10 of 28 suicide completers (36%) demonstrated significant decreases in different probe sets specific to TrkB.T1 in Brodmann areas 8 and 9. These findings were generalizable to other frontal regions but not to the cerebellum. The decrease in TrkB expression was specific to the T1 splice variant. Our results were not accounted for by substance comorbidity or by reduction in astrocyte number. We found no effect of genetic variation in a 2500-base pair promoter region or at relevant splice junctions; however, we detected an effect of methylation state at particular CpG dinucleotides on TrkB.T1 expression. CONCLUSION A reduction of TrkB.T1 expression in the frontal cortex of a subpopulation of suicide completers is associated with the methylation state of the promoter region.

Techne: Towards a New Generation of Requirements Modeling Languages with Goals, Preferences, and Inconsistency Handling

Techne is an abstract requirements modeling language that lays formal foundations for new modeling languages applicable during early phases of the requirements engineering process. During these phases, the requirements problem for the system-to-be is being structured, its candidate solutions described and compared in terms of how desirable they are to stakeholders. We motivate the need for Techne, introduce it through examples, and sketch its formalization.

A Framework for Empirical Evaluation of Model Comprehensibility

If designers of modelling languages want their creations to be used in real software projects, the communication qualities of their languages need to be evaluated, and their proposals must evolve as a result of these evaluations. A key quality of communication artifacts is their comprehensibility. We present a flexible framework to evaluate the comprehensibility of model representations that is grounded on the underlying theory of the language to be evaluated, and on theoretical frameworks in cognitive science.

Automated topic naming to support cross-project analysis of software maintenance activities

Researchers have employed a variety of techniques to extract underlying topics that relate to software development artifacts. Typically, these techniques use semi-unsupervised machine-learning algorithms to suggest candidate word-lists. However, word-lists are difficult to interpret in the absence of meaningful summary labels. Current topic modeling techniques assume manual labelling and do not use domainspecific knowledge to improve, contextualize, or describe results for the developers. We propose a solution: automated labelled topic extraction. Topics are extracted using Latent Dirichlet Allocation (LDA) from commit-log comments recovered from source control systems such as CVS and Bit-Keeper. These topics are given labels from a generalizable cross-project taxonomy, consisting of non-functional requirements. Our approach was evaluated with experiments and case studies on two large-scale RDBMS projects: MySQL and MaxDB. The case studies show that labelled topic extraction can produce appropriate, context-sensitive labels relevant to these projects, which provides fresh insight into their evolving software development activities.

Jambalaya: an interactive environment for exploring ontologies

This demonstration presents a visualization environment for exploring ontologies. An ontology defines a common vocabulary and structure of an information space for researchers and domain experts to exchange and share knowledge. A domain expert defines classes to represent concepts in a domain of discourse, with slots representing properties and relationships between the concepts. A class may be subclassed to represent more specific concepts. An ontology, together with a set of instances, constitutes a knowledge base. The Protégé tool [1] supports the modeling of ontologies to guide acquisition of content knowledge from subject-matter experts. In this demonstration, we present Jambalaya: the integration of a visualization tool called SHriMP [2] with Protégé.

Measure it? Manage it? Ignore it? software practitioners and technical debt

The technical debt metaphor is widely used to encapsulate numerous software quality problems. The metaphor is attractive to practitioners as it communicates to both technical and nontechnical audiences that if quality problems are not addressed, things may get worse. However, it is unclear whether there are practices that move this metaphor beyond a mere communication mechanism. Existing studies of technical debt have largely focused on code metrics and small surveys of developers. In this paper, we report on our survey of 1,831 participants, primarily software engineers and architects working in long-lived, software-intensive projects from three large organizations, and follow-up interviews of seven software engineers. We analyzed our data using both nonparametric statistics and qualitative text analysis. We found that architectural decisions are the most important source of technical debt. Furthermore, while respondents believe the metaphor is itself important for communication, existing tools are not currently helpful in managing the details. We use our results to motivate a technical debt timeline to focus management and tooling approaches.

Finding incremental solutions for evolving requirements

This paper investigates aspects of the problem of software evolution resulting from top-level requirements change. In particular, while most research on design for software focuses on finding some correct solution, this ignores that such a solution is often only correct in a particular, and often short-lived, context. Using a logic-based goal-oriented requirements modeling language, the paper poses the problem of finding desirable solutions as the requirements change. Among other possible criteria of desirability, we consider minimizing the effort required to implement the new solution, which involves reusing parts of the old solution. In general, the solution of requirements problems is viewed as an exploration using a “requirements engineering knowledge base” (REKB), whose specification is formalized. The paper reports on experience implementing the REKB on top of a so-called “reason-maintenance system”, and provides evidence that incremental solution finding is indeed more efficient.

Cognitive support for ontology modeling

Knowledge engineering tools are becoming ever more complex, and therefore increased cognitive support will be necessary to leverage the potential of those tools. Our paper motivates this claim by examining some previous work in this domain and explaining the nature of cognitive support. We discuss some of the problem areas we have encountered in our research. Through user questionnaires and observations carried out at the National Cancer Institute (NCI) and the University of Washington Foundational Model of Anatomy (FMA) Project, we have begun to gain an understanding of the cognitive barriers experienced by the users of knowledge engineering tools. We present some proposed solutions that could address the problems we identified, and in addition, discuss how our own tool, called Jambalaya, could be applied to provide cognitive support. We analyse the support Jambalaya provides using some non-functional design criteria and illustrate some trade-offs inherent in tool design. We suggest that the need for cognitive support in knowledge engineering is immediate and essential.

Performance Evaluation of NoSQL Databases: A Case Study

The choice of a particular NoSQL database imposes a specific distributed software architecture and data model, and is a major determinant of the overall system throughput. NoSQL database performance is in turn strongly influenced by how well the data model and query capabilities fit the application use cases, and so system-specific testing and characterization is required. This paper presents a method and the results of a study that selected among three NoSQL databases for a large, distributed healthcare organization. While the method and study considered consistency, availability, and partition tolerance (CAP) tradeoffs, and other quality attributes that influence the selection decision, this paper reports on the performance evaluation method and results. In our testing, a typical workload and configuration produced throughput that varied from 225 to 3200 operations per second between database products, while read operation latency varied by a factor of 5 and write latency by a factor of 4 (with the highest throughput product delivering the highest latency). We also found that achieving strong consistency reduced throughput by 10-25% compared to eventual consistency.

Agile requirements evolution via paraconsistent reasoning

Innovative companies need an agile approach for the engineering of their product requirements, to rapidly respond to and exploit changing conditions. The agile approach to requirements must nonetheless be systematic, especially with respect to accommodating legal and nonfunctional requirements. This paper examines how to support a combination of lightweight, agile requirements which can still be systematically modeled, analyzed and changed. We propose a framework, RE-KOMBINE, which is based on a propositional language for requirements modeling called Techne. We define operations on Techne models which tolerate the presence of inconsistencies in the requirements. This paraconsistent reasoning is vital for supporting delayed commitment to particular design solutions. We evaluate these operations with an industry case study using two well-known formal analysis tools. Our evaluations show that the proposed framework scales to industry-sized requirements models, while still retaining (via propositional logic) the informality that is so useful during early requirements analysis.