A Unification Algorithm For The First Order Theory of Quandles

The long-range goal of this project is to develop an algorithm to decide whether two terms are unifiable over the theory of quandles. First, it is shown that the general E-unification reduces to the E-matching problem due to the right-cancellation axioms of quandles. The E-matching process takes the general narrowing approach to equational matching. However, a naive application of narrowing is, at best, recursively enumerable and hence will not terminate given terms that do not match. This modification of narrowing places a hard limit on the use of the delta rules of the term rewriting system for quandles to ensure termination. It is implemented in the SWI-Prolog logic programming language. The question remains open as to whether the imposed limits still allow the program to find a unifier for all matching pairs

Creating Test Score Bands for Assessments Involving Ratings using a Generalizability Theory Approach to Reliability Estimation

The selection of a method for estimating the reliability of ratings has considerable implications for the use of assessments in personnel selection. In particular, the accuracy of corrections to validity coefficients for unreliability and test score bands are completely dependent on the correct estimation of the reliability. In this paper, we discuss how generalizability theory can be used to estimate reliability for test score bands with assessments involving ratings. Using selection data from a municipal entity, we demonstrate the use of generalizability theory-based compare the implications of its use in test score banding compared to the traditional approach

Lineage A betacoronavirus NS2 proteins and the homologous torovirus Berne pp1a carboxy-terminal domain are phosphodiesterases that antagonize activation of RNase L

Viruses in the family Coronaviridae, within the order Nidovirales, are etiologic agents of a range of human and animal diseases, including both mild and severe respiratory diseases in humans. These viruses encode conserved replicase and structural proteins as well as more diverse accessory proteins, encoded in the 3′ ends of their genomes, that often act as host cell antagonists. We previously showed that 2′,5′-phosphodiesterases (2′,5′-PDEs) encoded by the prototypical Betacoronavirus, mouse hepatitis virus (MHV), and by Middle East respiratory syndrome-associated coronavirus antagonize the oligoadenylate-RNase L (OAS-RNase L) pathway. Here we report that additional coronavirus superfamily members, including lineage A betacoronaviruses and toroviruses infecting both humans and animals, encode 2′,5′-PDEs capable of antagonizing RNase L. We used a chimeric MHV system (MHV(Mut)) in which exogenous PDEs were expressed from an MHV backbone lacking the gene for a functional NS2 protein, the endogenous RNase L antagonist. With this system, we found that 2′,5′-PDEs encoded by the human coronavirus HCoV-OC43 (OC43; an agent of the common cold), human enteric coronavirus (HECoV), equine coronavirus (ECoV), and equine torovirus Berne (BEV) are enzymatically active, rescue replication of MHV(Mut) in bone marrow-derived macrophages, and inhibit RNase L-mediated rRNA degradation in these cells. Additionally, PDEs encoded by OC43 and BEV rescue MHV(Mut) replication and restore pathogenesis in wild-type (WT) B6 mice. This finding expands the range of viruses known to encode antagonists of the potent OAS-RNase L antiviral pathway, highlighting its importance in a range of species as well as the selective pressures exerted on viruses to antagonize it. IMPORTANCE Viruses in the family Coronaviridae include important human and animal pathogens, including the recently emerged viruses severe acute respiratory syndrome-associated coronavirus (SARS-CoV) and Middle East respiratory syndrome-associated coronavirus (MERS-CoV). We showed previously that two viruses within the genus Betacoronavirus, mouse hepatitis virus (MHV) and MERS-CoV, encode 2′,5′-phosphodiesterases (2′,5′-PDEs) that antagonize the OAS-RNase L pathway, and we report here that these proteins are furthermore conserved among additional coronavirus superfamily members, including lineage A betacoronaviruses and toroviruses, suggesting that they may play critical roles in pathogenesis. As there are no licensed vaccines or effective antivirals against human coronaviruses and few against those infecting animals, identifying viral proteins contributing to virulence can inform therapeutic development. Thus, this work demonstrates that a potent antagonist of host antiviral defenses is encoded by multiple and diverse viruses within the family Coronaviridae, presenting a possible broad-spectrum therapeutic target

A Thermodynamic Model for Receptor Clustering

Intracellular signaling often arises from ligand-induced oligomerization of cell surface receptors. This oligomerization or clustering process is fundamentally a cooperative behavior between near-neighbor receptor molecules; the properties of this cooperative process clearly affects the signal transduction. Recent investigations have revealed the molecular basis of receptor-receptor interactions, but a simple theoretical framework for using this data to predict cluster formation has been lacking. Here, we propose a simple, coarse-grained, phenomenological model for ligand-modulated receptor interactions and discuss its equilibrium properties via mean-field theory. The existence of a first-order transition for this model has immediate implications regarding the robustness of the cellular signaling response.Comment: Biophysical Journal, to appea

Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

Abstract Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.http://deepblue.lib.umich.edu/bitstream/2027.42/78267/1/1748-5908-5-26.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/2/1748-5908-5-26.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/3/1748-5908-5-26-S3.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/4/1748-5908-5-26-S2.TIFFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78267/5/1748-5908-5-26-S1.TIFFPeer Reviewe