Benchmarking Utility Clean Energy Deployment: 2014

This report assembles data from more than 10 sources, including state Renewable Portfolio Standard (RPS) annual reports, U.S. Securities and Exchange Commission 10-K filings and Public Utility Commission reports, to show how 32 of the largest U.S. investor-owned electric utility holding companies stack up on renewable energy and energy efficiency

Systems approaches to drug repositioning

PhD ThesisDrug discovery has overall become less fruitful and more costly, despite vastly increased biomedical knowledge and evolving approaches to Research and Development (R&D). One complementary approach to drug discovery is that of drug repositioning which focusses on identifying novel uses for existing drugs. By focussing on existing drugs that have already reached the market, drug repositioning has the potential to both reduce the timeframe and cost of getting a disease treatment to those that need it. Many marketed examples of repositioned drugs have been found via serendipitous or rational observations, highlighting the need for more systematic methodologies. Systems approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but require an integrative approach to biological data. Integrated networks can facilitate systems-level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person can identify portions of the graph that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated procedures are required to mine integrated networks systematically for these subgraphs and bring them to the attention of the user. The aim of this project was the development of novel computational methods to identify new therapeutic uses for existing drugs (with particular focus on active small molecules) using data integration. A framework for integrating disparate data relevant to drug repositioning, Drug Repositioning Network Integration Framework (DReNInF) was developed as part of this work. This framework includes a high-level ontology, Drug Repositioning Network Integration Ontology (DReNInO), to aid integration and subsequent mining; a suite of parsers; and a generic semantic graph integration platform. This framework enables the production of integrated networks maintaining strict semantics that are important in, but not exclusive to, drug repositioning. The DReNInF is then used to create Drug Repositioning Network Integration (DReNIn), a semantically-rich Resource Description Framework (RDF) dataset. A Web-based front end was developed, which includes a SPARQL Protocol and RDF Query Language (SPARQL) endpoint for querying this dataset. To automate the mining of drug repositioning datasets, a formal framework for the definition of semantic subgraphs was established and a method for Drug Repositioning Semantic Mining (DReSMin) was developed. DReSMin is an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. The ability of DReSMin to identify novel Drug-Target (D-T) associations was investigated. 9,643,061 putative D-T interactions were identified and ranked, with a strong correlation between highly scored associations and those supported by literature observed. The 20 top ranked associations were analysed in more detail with 14 found to be novel and six found to be supported by the literature. It was also shown that this approach better prioritises known D-T interactions, than other state-of-the-art methodologies. The ability of DReSMin to identify novel Drug-Disease (Dr-D) indications was also investigated. As target-based approaches are utilised heavily in the field of drug discovery, it is necessary to have a systematic method to rank Gene-Disease (G-D) associations. Although methods already exist to collect, integrate and score these associations, these scores are often not a reliable re flection of expert knowledge. Therefore, an integrated data-driven approach to drug repositioning was developed using a Bayesian statistics approach and applied to rank 309,885 G-D associations using existing knowledge. Ranked associations were then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network it was shown that diseases of the central nervous system (CNS) provide an area of interest. The network was then systematically mined for semantic subgraphs that capture novel Dr-D relations. 275,934 Dr-D associations were identified and ranked, with those more likely to be side-effects filtered. Work presented here includes novel tools and algorithms to enable research within the field of drug repositioning. DReNIn, for example, includes data that previous comparable datasets relevant to drug repositioning have neglected, such as clinical trial data and drug indications. Furthermore, the dataset may be easily extended using DReNInF to include future data as and when it becomes available, such as G-D association directionality (i.e. is the mutation a loss-of-function or gain-of-function). Unlike other algorithms and approaches developed for drug repositioning, DReSMin can be used to infer any types of associations captured in the target semantic network. Moreover, the approaches presented here should be more generically applicable to other fields that require algorithms for the integration and mining of semantically rich networks.European and Physical Sciences Research Council (EPSRC) and GS

Casework Treatment Procedures as a Function of Client-Diagnostic Variables: A Study of Their Relationship in the Casework Interview

The study is an exploratory examination of the relationship between the psychosocial diagnostic evaluation and the treatment procedures used by the caseworker in the interview. The psychosocial diagnosis has been defined by twenty-four selected variables assumed to be relevant indicators of the diagnostic process. The treatment procedures have been defined by the Hollis' typology of casework treatment. The variation in the use of the treatment procedures is also examined in relation to three intervening variables: (1) treatment phase; (2) casework method (supportive vs. modifying); and, (3) caseworker. The study is based upon a secondary analysis of data originally collected for the Casework Methods Project, Center for Social Casework Research, Community Service Society of New York. The clients studied are a well defined group. The sample represents motivated, lower-middle class, Negro and White clients of slightly above average general intelligence living in intact families and seeking assistance from a private family agency for difficulties in marital and/or parent-child relationships. The sample tends to represent clients who continue in service through at least the ninth assigned service interview. These are clients who have agreed to partake in a research project and to have their interviews tape recorded. The study has examined eighty-seven tape recorded interviews drawn from thirty-five clients representing twenty-two families. Individual clients are represented by a range of from one to three interviews drawn from a maximum of three phases of treatment. The interviews are representative of assigned service client interview one through fourteen and assigned service case interview one through thirty-nine. The caseworkers treating the clients assessed the clients' status and functioning on the selected diagnostic variables. The treatment procedures used by that same caseworker with each client were determined through the content analysis of tape recorded interviews with the clients. Each worker statement (clause) was classified as one of eleven possible treatment procedures. The proportionate use of each procedure was computed for each interview. Differences in proportions were examined in relation to the independent variables. Variation in the use of the treatment procedures in relation to the independent variables of treatment phase, casework method, and case- worker were examined through a series of multivariate analyses of variance tests. The associations between the twenty-four diagnostic variables and the eleven procedures were assessed through a correlational analysis. In addition the twenty-four diagnostic variables were factor analyzed. Three hypothetical components were identified. Factor scores were computed for each client on each of the three components and correlated with the treatment procedures used with the clients. Non-parametric techniques were used for supplementary analysis. The general hypothesis that the procedures are associated with the diagnostic variables is partially confirmed for nine of the eleven procedures in the sense that a larger number of significant correlations occur than attributable to chance. However, the amount of variation explained by the diagnostic indicators is generally rather small. The degree of the associations are from weak to moderate. The theoretically expected associations tend to occur although to an extent less than anticipated. The largest amount of variation in the use of the treatment procedures was explained by differences among caseworkers. Differences among treatment phases explained a significant amount of the variation in one of the procedures. The writer anticipates that control for caseworker and phase would increase the diagnostic-treatment associations. In addition to the testing of the study hypotheses the study describes the treatment process in this sample of eighty-seven tape recorded interviews

Thermal mechanical analysis of sprag clutches

Work done at Case Western Reserve University on the Thermal Mechanical analysis of sprag helicopter clutches is reported. The report is presented in two parts. The first part is a description of a test rig for the measurement of the heat generated by high speed sprag clutch assemblies during cyclic torsional loading. The second part describes a finite element modeling procedure for sliding contact. The test rig provides a cyclic torsional load of 756 inch-pounds at 5000 rpm using a four-square arrangement. The sprag clutch test unit was placed between the high speed pinions of the circulating power loop. The test unit was designed to have replaceable inner ad outer races, which contain the instrumentation to monitor the sprag clutch. The torque loading device was chosen to be a water cooled magnetic clutch, which is controlled either manually or through a computer. In the second part, a Generalized Eulerian-Lagrangian formulation for non-linear dynamic problems is developed for solid materials. This formulation is derived from the basic laws and axioms of continuum mechanics. The novel aspect of this method is that we are able to investigate the physics in the spatial region of interest as material flows through it without having to follow material points. A finite element approximation to the governing equations is developed. Iterative Methods for the solution of the discrete finite element equations are explored. A FORTRAN program to implement this formulation is developed and a number of solutions to problems of sliding contact are presented

Medically Unnecessary Female Genital Alteration: Implications for Health Care Workers

Graduate LUO Remote Textual or Investigativ

Structural Equation Modelling: Guidelines for Determining Model Fit

The following paper presents current thinking and research on fit indices for structural equation modelling. The paper presents a selection of fit indices that are widely regarded as the most informative indices available to researchers. As well as outlining each of these indices, guidelines are presented on their use. The paper also provides reporting strategies of these indices and concludes with a discussion on the future of fit indices

BacillOndex: An Integrated Data Resource for Systems and Synthetic Biology

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism