Development of a combined operational and strategic decision support model for offshore wind

As offshore wind farms are built in increasingly remote and difficult to access locations, there is an increased cost and risk associated with operations and maintenance. This results from operating in harsher climates, the need to use specialised vessels, large losses associated with down time and greater technical uncertainty from using wind turbines without a significant operating history. There is a need for developers to understand how operational parameters influence overall costs and the exposure to risk associated with different working strategies. By linking a detailed operational model with decision making analysis, it is possible to identify the optimal developer strategy at different times throughout the wind farm life. This paper presents a modelling framework to achieve this objective as well as an illustrative case study

Quality improvement of community pharmacy services:a prioritisation exercise

Funded by NHS Education for ScotlandPeer reviewedPublisher PD

Co-chaperones are limiting in a depleted chaperone network

To probe the limiting nodes in the chaperoning network which maintains cellular proteostasis, we expressed a dominant negative mutant of heat shock factor 1 (dnHSF1), the regulator of the cytoplasmic proteotoxic stress response. Microarray analysis of non-stressed dnHSF1 cells showed a two- or more fold decrease in the transcript level of 10 genes, amongst which are the (co-)chaperone genes HSP90AA1, HSPA6, DNAJB1 and HSPB1. Glucocorticoid signaling, which requires the Hsp70 and the Hsp90 folding machines, was severely impaired by dnHSF1, but fully rescued by expression of DNAJA1 or DNAJB1, and partially by ST13. Expression of DNAJB6, DNAJB8, HSPA1A, HSPB1, HSPB8, or STIP1 had no effect while HSP90AA1 even inhibited. PTGES3 (p23) inhibited only in control cells. Our results suggest that the DNAJ co-chaperones in particular become limiting in a depleted chaperoning network. Our results also suggest a difference between the transcriptomes of cells lacking HSF1 and cells expressing dnHSF1

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Engineering human MEK-1 for structural studies: a case study of combinatorial domain hunting

Structural biology studies typically require large quantities of pure, soluble protein. Currently the most widely-used method for obtaining such protein involves the use of bioinformatics and experimental methods to design constructs of the target, which are cloned and expressed. Recently an alternative approach has emerged, which involves random fragmentation of the gene of interest and screening for well-expressing fragments. Here we describe the application of one such fragmentation method, combinatorial domain hunting (CDH), to a target which historically was difficult to express, human MEK-1. We show how CDH was used to identify a fragment which covers the kinase domain of MEK-1 and which expresses and crystallizes significantly better than designed expression constructs, and we report the crystal structure of this fragment which explains some of its superior properties. Gene fragmentation methods, such as CDH, thus hold great promise for tackling difficult-to-express target proteins

State-set branching:Leveraging BDDs for heuristic search

In this article, we present a framework called state-set branching that combines symbolic search based on reduced ordered Binary Decision Diagrams (BDDs) with best-first search, such as A * and greedy best-first search. The framework relies on an extension of these algorithms from expanding a single state in each iteration to expanding a set of states. We prove that it is generally sound and optimal for two A* implementations and show how a new BDD technique called branching partitioning can be used to efficiently expand sets of states. The framework is general. It applies to any heuristic function, evaluation function, and transition cost function defined over a finite domain. Moreover, branching partitioning applies to both disjunctive and conjunctive transition relation partitioning. An extensive experimental evaluation of the two A * implementations proves state-set branching to be a powerful framework. The algorithms outperform the ordinary A * algorithm in almost all domains. In addition, they can improve the complexity of A * exponentially and often dominate both A * and blind BDD-based search by several orders of magnitude. Moreover, they have substantially better performance than BDDA*, the currently most efficient BDD-based implementation of A*