Individual sequences in large sets of gene sequences may be distinguished efficiently by combinations of shared sub-sequences

BACKGROUND: Most current DNA diagnostic tests for identifying organisms use specific oligonucleotide probes that are complementary in sequence to, and hence only hybridise with the DNA of one target species. By contrast, in traditional taxonomy, specimens are usually identified by 'dichotomous keys' that use combinations of characters shared by different members of the target set. Using one specific character for each target is the least efficient strategy for identification. Using combinations of shared bisectionally-distributed characters is much more efficient, and this strategy is most efficient when they separate the targets in a progressively binary way. RESULTS: We have developed a practical method for finding minimal sets of sub-sequences that identify individual sequences, and could be targeted by combinations of probes, so that the efficient strategy of traditional taxonomic identification could be used in DNA diagnosis. The sizes of minimal sub-sequence sets depended mostly on sequence diversity and sub-sequence length and interactions between these parameters. We found that 201 distinct cytochrome oxidase subunit-1 (CO1) genes from moths (Lepidoptera) were distinguished using only 15 sub-sequences 20 nucleotides long, whereas only 8–10 sub-sequences 6–10 nucleotides long were required to distinguish the CO1 genes of 92 species from the 9 largest orders of insects. CONCLUSION: The presence/absence of sub-sequences in a set of gene sequences can be used like the questions in a traditional dichotomous taxonomic key; hybridisation probes complementary to such sub-sequences should provide a very efficient means for identifying individual species, subtypes or genotypes. Sequence diversity and sub-sequence length are the major factors that determine the numbers of distinguishing sub-sequences in any set of sequences

Minimizing the cost of fault location when testing from a finite state machine

If a test does not produce the expected output, the incorrect output may have been caused by an earlier state transfer failure. Ghedamsi and coworkers generate a set of candidates and then produce further tests to locate the failures within this set. We consider a special case where there is a state identification process that is known to be correct. A number of preset and adaptive approaches to fault location are described and the problem of minimizing the cost is explored. Some of the approaches lead to NP-hard optimization problems for which possible heuristics are suggested

Combined Diagnostic Modalities Improve Detection of Detrusor External Sphincter Dyssynergia

Introduction. The diagnosis of detrusor-external sphincter dyssynergia (DESD) is a clinically relevant finding during urodynamic testing. However, there is no consensus regarding diagnostic specifics of electromyography (EMG) or voiding cystourethrography (VCUG). We evaluated the concordance of the two modalities most commonly used in clinical practice for the diagnosis of DESD. Methods. Patients were prospectively evaluated by a single urodynamicist at an academic center and retrospectively re-evaluated by an independent urodynamicist for agreement. DESD was determined by increased patch EMG activity or a dilated bladder neck/proximal urethra on VCUG during detrusor contraction. Minimal acceptable criterion for agreement was set at 70%. Results. Forty-six patients were diagnosed with DESD with both modalities available. Of these 46 patients, 25 were diagnosed by both tests, 11 by VCUG alone and 10 by patch EMG alone. Binomial testing demonstrated the proportion of agreement was 54% (95% CI 39% to 68%). Conclusion. We found significant disagreement between the two modalities, similar to previously reported findings using needle EMG, and we expand the applicability of our data to the majority of clinicians who use patch EMG electrodes. This further supports the idea that the combined use of EMG and VCUG for diagnosis can identify more cases of DESD than either modality alone

DNA as Patentable Subject Matter and a Narrow Framework for Addressing the Perceived Problems Caused by Gene Patents

Concerns about the alleged harmful effects of gene patents— including hindered research and innovation and impeded patient access to high-quality genetic diagnostic tests—have resulted in overreactions from the public and throughout the legal profession. These overreactions are exemplified by Association for Molecular Pathology v. U.S. Patent and Trademark Office, a 2010 case in the Southern District of New York that held that isolated DNA is unpatentable subject matter under 35 U.S.C. § 101. The problem with these responses is that they fail to adequately consider the role that gene patents and patents on similar biomolecules play in facilitating investment in the costly and risky developmental processes required to transform the underlying inventions into marketable products. Accordingly, a more precisely refined solution is advisable. This Note proposes a narrowly tailored set of solutions to address the concerns about gene patents without destroying the incentives for companies to create and commercialize inventions derived from these and similar patents

Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required

Increasing the impact of mathematics support on aiding student transition in higher education

The ever growing gap between secondary and university level mathematics is a major concern to higher education institutions. The increase in diversity of students’ background in mathematics, with entry qualifications ranging from the more traditional A-level programmes to BTEC or international qualifications is compounded where institutions attempt to widen participation. For example, work-based learners may have been out of education for prolonged periods, and consequently, are often unprepared for the marked shift in levels, and catering for all abilities is difficult in the normal lecture, tutorial format. Lack of sufficient mathematical knowledge not only affects students’ achievement on courses but also leads to disengagement and higher drop-out rates during the first two years of study. Many universities now offer a maths support service in an attempt to overcome these issues, but their success is varied. This paper presents a novel approach to maths support designed and adopted by the University of Lincoln, School of Engineering, to bridge this transition gap for students, offer continued support through assessment for learning (AFL) and Individual Learning Plans (ILP’s), and ultimately increase student achievement, engagement and retention. The paper then extends this proven approach and discusses recently implemented enhancements through the use of on-line diagnostic testing and a ‘student expert’ system to harness mathematical knowledge held by those gifted and talented students (often overlooked by higher education institutions) and to promote peer-to-peer mentoring. The paper shows that with the proven system in place, there is a marked increase in student retention compared with national benchmark data, and an increase in student engagement and achievement measured through student feedback and assessments. Although the on-line enhancements are in the early stages of implementation it is expected, based on these results, that further improvements will be shown