Chief Joseph Kokanee Enhancement Project : Strobe Light Deterrent Efficacy Test and Fish Behavior Determination at Grond Coulee Dam Third Powerplant Forebay.

Since 1995, the Colville Confederated Tribes have managed the Chief Joseph Kokanee Enhancement Project as part of the Northwest Power Planning Council's (NWPPC) Fish and Wildlife Program. Project objectives have focused on understanding natural production of kokanee (a land-locked sockeye salmon) and other fish stocks in the area above Grand Coulee and Chief Joseph Dams on the Columbia River. A 42-month investigation concluded that entrainment at Grand Coulee Dam ranged from 211,685 to 576,676 fish annually. Further analysis revealed that 85% of the total entrainment occurred at the dam's third powerplant. These numbers represent a significant loss to the tribal fisheries upstream of the dam. In response to a suggestion by the NWPPC's Independent Scientific Review Panel, the scope of work for the Chief Joseph Kokanee Enhancement Project was expanded to include a multiyear pilot test of a strobe light system to help mitigate fish entrainment. This report details the work conducted during the first year of the study by researchers of the Colville Confederated Tribes in collaboration with the Pacific Northwest National Laboratory (PNNL). The objective of the study was to determine the efficacy of a prototype strobe light system to elicit a negative phototactic response in kokanee and rainbow trout. Analysis of the effect of strobe lights on the distribution (numbers) and behavior of kokanee and rainbow trout was based on 51, 683 fish targets detected during the study period (June 30 through August 1, 2001). Study findings include the following: (1) Analysis of the count data indicated that significantly more fish were present when the lights were on compared to off. This was true for both the 24-hr tests as well as the 1-hr tests. Powerplant discharge, distance from lights, and date were significant factors in the analysis. (2) Behavioral results indicated that fish within 14 m of the lights were trying to avoid the lights by swimming across the lighted region or upstream. Fish were also swimming faster and straighter when the lights were on compared to off. (3) The behavioral results were most pronounced for medium- and large-sized fish at night. Medium-sized fish, based on acoustic target strength, were similar to the size of kokanee and rainbow trout released upstream of Grand Coulee Dam. Based on this study and general review of strobe lights, the researchers recommend several modifications and enhancements to the follow-on study in 2002. The recommendations include: (1) modifying the study design to include only the 24-hr on/off treatments, and controlling the discharge at the third powerplant, so it can be included as a design variable; and (2) providing additional data by beginning the study earlier (mid-May) to better capture the kokanee population, deploying an additional splitbeam transducer to sample the region close to the lights, and increasing the number of lights to provide better definition of the lit and unlit region

Estimating the incidence, prevalence and true cost of asthma in the UK: secondary analysis of national stand-alone and linked databases in England, Northern Ireland, Scotland and Wales-a study protocol.

INTRODUCTION: Asthma is now one of the most common long-term conditions in the UK. It is therefore important to develop a comprehensive appreciation of the healthcare and societal costs in order to inform decisions on care provision and planning. We plan to build on our earlier estimates of national prevalence and costs from asthma by filling the data gaps previously identified in relation to healthcare and broadening the field of enquiry to include societal costs. This work will provide the first UK-wide estimates of the costs of asthma. In the context of asthma for the UK and its member countries (ie, England, Northern Ireland, Scotland and Wales), we seek to: (1) produce a detailed overview of estimates of incidence, prevalence and healthcare utilisation; (2) estimate health and societal costs; (3) identify any remaining information gaps and explore the feasibility of filling these and (4) provide insights into future research that has the potential to inform changes in policy leading to the provision of more cost-effective care. METHODS AND ANALYSIS: Secondary analyses of data from national health surveys, primary care, prescribing, emergency care, hospital, mortality and administrative data sources will be undertaken to estimate prevalence, healthcare utilisation and outcomes from asthma. Data linkages and economic modelling will be undertaken in an attempt to populate data gaps and estimate costs. Separate prevalence and cost estimates will be calculated for each of the UK-member countries and these will then be aggregated to generate UK-wide estimates. ETHICS AND DISSEMINATION: Approvals have been obtained from the NHS Scotland Information Services Division's Privacy Advisory Committee, the Secure Anonymised Information Linkage Collaboration Review System, the NHS South-East Scotland Research Ethics Service and The University of Edinburgh's Centre for Population Health Sciences Research Ethics Committee. We will produce a report for Asthma-UK, submit papers to peer-reviewed journals and construct an interactive map

Implementing telephone triage in general practice: a process evaluation of a cluster randomised controlled trial

Background: Telephone triage represents one strategy to manage demand for face-to-face GP appointments in primary care. However, limited evidence exists of the challenges GP practices face in implementing telephone triage. We conducted a qualitative process evaluation alongside a UK-based cluster randomised trial (ESTEEM) which compared the impact of GP-led and nurse-led telephone triage with usual care on primary care workload, cost, patient experience, and safety for patients requesting a same-day GP consultation. The aim of the process study was to provide insights into the observed effects of the ESTEEM trial from the perspectives of staff and patients, and to specify the circumstances under which triage is likely to be successfully implemented. Here we report perspectives of staff. Methods: The intervention comprised implementation of either GP-led or nurse-led telephone triage for a period of 2-3 months. A qualitative evaluation was conducted using staff interviews recruited from eight general practices (4 GP triage, 4 Nurse triage) in the UK, implementing triage as part of the ESTEEM trial. Qualitative interviews were undertaken with 44 staff members in GP triage and nurse triage practices (16 GPs, 8 nurses, 7 practice managers, 13 administrative staff). Results: Staff reported diverse experiences and perceptions regarding the implementation of telephone triage, its effects on workload, and on the benefits of triage. Such diversity were explained by the different ways triage was organised, the staffing models used to support triage, how the introduction of triage was communicated across practice staff, and by how staff roles were reconfigured as a result of implementing triage. Conclusion: The findings from the process evaluation offer insight into the range of ways GP practices participating in ESTEEM implemented telephone triage, and the circumstances under which telephone triage can be successfully implemented beyond the context of a clinical trial. Staff experiences and perceptions of telephone triage are shaped by the way practices communicate with staff, prepare for and sustain the changes required to implement triage effectively, as well as by existing practice culture, and staff and patient behaviour arising in response to the changes made. Trial registration: Current Controlled Trials ISRCTN20687662. Registered 28 May 2009

Estimating Population Abundance with a Mixture of Physical Capture and PIT Tag Antenna Detection Data

The inclusion of passive interrogation antenna (PIA) detection data has promise to increase precision of population abundance estimates (Nˆ ). However, encounter probabilities are often higher for PIAs than for physical capture. If the difference is not accounted for, Nˆ may be biased. Using simulations, we estimated the magnitude of bias resulting from mixed capture and detection probabilities and evaluated potential solutions for removing the bias for closed capture models. Mixing physical capture and PIA detections (pdet) resulted in negative biases in Nˆ . However, using an individual covariate to model differences removed bias and improved precision. From a case study of fish making spawning migrations across a stream-wide PIA (pdet ≤ 0.9), the coefficient of variation (CV) of Nˆ declined 39%–82% when PIA data were included, and there was a dramatic reduction in time to detect a significant change in Nˆ . For a second case study, with modest pdet (≤0.2) using smaller PIAs, CV (Nˆ ) declined 4%–18%. Our method is applicable for estimating abundance for any situation where data are collected with methods having different capture–detection probabilities

Pathway to the PiezoElectronic Transduction Logic Device

The information age challenges computer technology to process an exponentially increasing computational load on a limited energy budget - a requirement that demands an exponential reduction in energy per operation. In digital logic circuits, the switching energy of present FET devices is intimately connected with the switching voltage, and can no longer be lowered sufficiently, limiting the ability of current technology to address the challenge. Quantum computing offers a leap forward in capability, but a clear advantage requires algorithms presently developed for only a small set of applications. Therefore, a new, general purpose, classical technology based on a different paradigm is needed to meet the ever increasing demand for data processing.Comment: in Nano Letters (2015

A video life-world approach to consultation practice: The relevance of a socio-phenomenological approach

This article discusses the [development and] use of a video life-world schema to explore alternative orientations to the shared health consultation. It is anticipated that this schema can be used by practitioners and consumers alike to understand the dynamics of videoed health consultations, the role of the participants within it and the potential to consciously alter the outcome by altering behaviour during the process of interaction. The study examines health consultation participation and develops an interpretative method of analysis that includes image elicitation (via videos), phenomenology (to identify the components of the analytic framework), narrative (to depict the stories of interactions) and a reflexive mode (to develop shared meaning through a conceptual framework for analysis). The analytic framework is derived from a life-world conception of human mutual shared interaction which is presented here as a novel approach to understanding patient-centred care. The video materials used in this study were derived from consultations in a Walk-in Centre (WiC) in East London. The conceptual framework produced through the process of video analysis is comprised of different combinations of movement, knowledge and emotional conversations that are used to classify objective or engaged WiC health care interactions. The videoed interactions organise along an active or passive, facilitative or directive typical situation continuum illustrating different kinds of textual approaches to practice that are in tension or harmony. The schema demonstrates how practitioners and consumers interact to produce these outcomes and indicates the potential for both consumers and practitioners to be educated to develop practice dynamics that support patient-centred care and impact on health outcomes

Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data

Background: Telemonitoring of symptoms and physiological signs has been suggested as a means of early detection of exacerbations of chronic obstructive pulmonary disease (COPD) with a view to instituting timely treatment. However, current algorithms to identify exacerbations result in frequent false positive results and increased workload. Machine learning, when applied to predictive modelling, can determine patterns of risk factors useful for improving quality of predictions. Objective: To establish if machine learning techniques applied to telemonitoring datasets improve prediction of hospital admissions, decisions to start steroids, and to determine if the addition of weather data further improves such predictions. Methods: We used daily symptoms, physiological measures and medication data, with baseline demography, COPD severity, quality of life, and hospital admissions from a pilot and large randomised controlled trial of telemonitoring in COPD. In addition, we linked weather data from the UK Meteorological Office. We used feature selection and extraction techniques for time-series to construct up to 153 predictive patterns (features) from symptom, medication, and physiological measurements. The resulting variables were used for the construction of predictive models fitted to training sets of patients and compared to common algorithms. Results: We had a mean 363 days of telemonitoring data from 135 patients. The two most practical traditional score-counting algorithms, restricted to cases with complete data resulted in AUC estimates of 0.60 [CI 95% 0.51, 0.69] and 0.58 [0.50, 0.67] for predicting admissions based on a single day’s readings. However, in a real-world scenario allowing for missing data, with greater numbers of patient daily data and hospitalisations (N = 57,150, N+=17), the performance of all the traditional algorithms fell, including those based on two days data. One of the most frequently used algorithms performed no better than chance. Machine learning models demonstrated significant improvements; the best machine learning algorithm based on 57,150 episodes resulted in an aggregated AUC = 0.73 [0.67, 0.79]. Addition of weather data measurements resulted in a negligible improvement in the predictive performance of the best model (AUC = 0.74 [0.69, 0.79]). In order to achieve an 80% true positive rate (sensitivity), the traditional algorithms were associated with an 80% false positive rate: our algorithm halved this rate to approximately 40% (specificity approximately 60%). The machine learning algorithm was moderately superior to the best standard algorithm (AUC = 0.77 [0.74, 0.79] v AUC = 0.66 [0.63, 0.68]) at predicting the need for steroids. Conclusions: The early detection and management of COPD remains an important goal given the huge personal and economic costs of the condition. Machine learning approaches, which can be tailored to an individual’s baseline profile and can learn from experience of the individual patient are superior to existing predictive algorithms show promise in achieving this goal