The multiple location time weighted index: Using patient activity spaces to calculate primary care service areas

Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. ^ During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. ^ Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering "free or low cost visits", meeting "all of the patient's health care needs", and seeing "the patient quickly" were all ranked higher than geographic reasons. ^ Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts. ^

The Multiple Roles of Cyk1p in the Assembly and Function of the Actomyosin Ring in Budding Yeast

The budding yeast IQGAP-like protein Cyk1p/Iqg1p localizes to the mother-bud junction during anaphase and has been shown to be required for the completion of cytokinesis. In this study, video microscopy analysis of cells expressing green fluorescent protein-tagged Cyk1p/Iqg1p demonstrates that Cyk1p/Iqg1p is a dynamic component of the contractile ring during cytokinesis. Furthermore, in the absence of Cyk1p/Iqg1p, myosin II fails to undergo the contraction-like size change at the end of mitosis. To understand the mechanistic role of Cyk1p/Iqg1p in actomyosin ring assembly and dynamics, we have investigated the role of the structural domains that Cyk1p/Iqg1p shares with IQGAPs. An amino terminal portion containing the calponin homology domain binds to actin filaments and is required for the assembly of actin filaments to the ring. This result supports the hypothesis that Cyk1p/Iqg1p plays a direct role in F-actin recruitment. Deletion of the domain harboring the eight IQ motifs abolishes the localization of Cyk1p/Iqg1p to the bud neck, suggesting that Cyk1p/Iqg1p may be localized through interactions with a calmodulin-like protein. Interestingly, deletion of the COOH-terminal GTPase-activating protein-related domain does not affect Cyk1p/Iqg1p localization or actin recruitment to the ring but prevents actomyosin ring contraction. In vitro binding experiments show that Cyk1p/Iqg1p binds to calmodulin, Cmd1p, in a calcium-dependent manner, and to Tem1p, a small GTP-binding protein previously found to be required for the completion of anaphase. These results demonstrate the critical function of Cyk1p/Iqg1p in regulating various steps of actomyosin ring assembly and cytokinesis.

A unique human gene that spans over 230 kb in the human chromosome 8p11-12 and codes multiple family proteins sharing RNA-binding motifs.

A unique gene, RBP-MS, spanning over 230 kb in the human chromosome 8p11-12 near the Werner syndrome gene locus is described. The single-copy RBP-MS gene is alternatively spliced, resulting in a family of at least 12 transcripts (average length of 1.5 kb). Nine different types of cDNAs that encode an RNa-binding motif at the N terminus and helix-rich sequences at the C terminus have been identified thus far. Among the 16 exons identified, four 5'-proximal exons contained sequences homologous to the RNA-binding domain of Drosophila couch potato gene. Northern blot analysis showed that the RBP-MS gene was expressed strongly in the heart, prostate, intestine, and ovary, and poorly in the skeletal muscle, spleen, thymus, brain, and peripheral leukocytes. The possible role of this gene in RNA metabolism is discussed.

The multiple phenotypes of allosteric receptor mutants.

Channel-linked neurotransmitter receptors are membrane-bound heterooligomers made up of distinct, although homologous, subunits. They mediate chemo-electrical signal transduction and its regulation via interconversion between multiple conformations that exhibit distinct pharmacological properties and biological activities. The large diversity of functional properties and the widely pleiotropic phenotypes, which arise from point mutations in their subunits (or from subunit substitutions), are interpreted in terms of an allosteric model that incorporates multiple discrete conformational states. The model predicts that three main categories of phenotypes may result from point mutations, altering selectively one (or more) of the following features: (i) the properties of individual binding sites (K phenotype), (ii) the biological activity of the ion channel (gamma phenotype) of individual conformations, or (iii) the isomerization constants between receptor conformations (L phenotype). Several nicotinic acetylcholine and glycine receptor mutants with complex phenotypes are quantitatively analyzed in terms of the model, and the analogies among phenotypes are discussed.

The undying one : and other poems /

Mode of access: Internet.

Report and evaluation of "When is September," [an experiment in the provision of pre school experiences via educational television to orient and motivate children entering grade one and to facilitate their adjustment to group activities] Title VII, Project number 120, National Defense Education Act, grant number 708004.09.

Mode of access: Internet.

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches.