Functional reasoning in diagnostic problem solving

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base

The Trichoptera barcode initiative: a strategy for generating a species-level Tree of Life

DNA barcoding was intended as a means to provide species-level identifications through associating DNA sequences from unknown specimens to those from curated reference specimens. Although barcodes were not designed for phylogenetics, they can be beneficial to the completion of the Tree of Life. The barcode database for Trichoptera is relatively comprehensive, with data from every family, approximately two-thirds of the genera, and one-third of the described species. Most Trichoptera, as with most of life’s species, have never been subjected to any formal phylogenetic analysis. Here, we present a phylogeny with over 16 000 unique haplotypes as a working hypothesis that can be updated as our estimates improve. We suggest a strategy of implementing constrained tree searches, which allow larger datasets to dictate the backbone phylogeny, while the barcode data fill out the tips of the tree. We also discuss how this phylogeny could be used to focus taxonomic attention on ambiguous species boundaries and hidden biodiversity. We suggest that systematists continue to differentiate between ‘Barcode Index Numbers’ (BINs) and ‘species’ that have been formally described. Each has utility, but they are not synonyms. We highlight examples of integrative taxonomy, using both barcodes and morphology for species description. This article is part of the themed issue ‘From DNA barcodes to biomes’

Evaluation of a Real-Time Nucleic Acid Sequence-Based Amplification Assay Using Molecular Beacons for Detection of Human Immunodeficiency Virus Type 1

We evaluated the performance characteristics of a new, real-time nucleic acid sequence-based amplification (NASBA) assay that incorporates molecular beacon technology for detection of human immunodeficiency virus type 1 (HIV-1). The quantitative results were comparable to those obtained with three leading commercially available assays. The analytical sensitivity was 37 IU/ml. The NASBA assay detected clinically relevant recombinant viruses and all group M HIV-1 subtypes

The −48 C/T polymorphism in the presenilin 1 promoter is associated with an increased risk of developing Alzheimer's disease and an increased Aβ load in brain

Mutations in the presenilin 1 gene (PS1) account for the majority of early onset, familial, autosomal dominant forms of Alzheimer's disease (AD), whereas its role in other late onset forms of AD remains unclear. A −48 C/T polymorphism in the PS1 promoter has been associated with an increased genetic risk in early onset complex AD and moreover has been shown to influence the expression of the PS1 gene. This raises the possibility that previous conflicting findings from association studies with homozygosity for the PS1 intron 8 polymorphism might be the result of linkage disequilibrium with the -48 CC genotype. Here we provide further evidence of increased risk of AD associated with homozygosity for the −48 CC genotype (odds ratio=1.6). We also report a phenotypic correlation with Aβ(40), Aβ(42(43)), and total Aβ load in AD brains. The −48 CC genotype was associated with 47% greater total Aβ load (p<0.003) compared to CT + TT genotype bearers. These results suggest that the -48 C/T polymorphism in the PS1 promoter may increase the risk of AD, perhaps by altering PS1 gene expression and thereby influencing Aβ load.


Keywords: Alzheimer; presenilin; promoter; polymorphis