An aggregated wind power generation model based on MERRA reanalysis data: MATLAB model and example data for the April 2014 wind farm distribution of Great Britain.

The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014

The impact of future offshore wind farms on wind power generation in Great Britain

In the coming years the geographical distribution of wind farms in Great Britain is expected to change significantly. Following the development of the “round 3” wind zones (circa 2025), most of the installed capacity will be located in large offshore wind farms. However, the impact of this change in wind-farm distribution on the characteristics of national wind generation is largely unknown. This study uses a 34-year reanalysis dataset (Modern-Era Retrospective Analysis for Research and Applications (MERRA) from National Aeronautics and Space Administration, Global Modeling and Assimilation Office (NASA-GMAO)) to produce a synthetic hourly time series of GB-aggregated wind generation based on: (1) the “current” wind farm distribution; and (2) a “future” wind farm distribution scenario. The derived data are used to estimate a climatology of extreme wind power events in Great Britain for each wind farm distribution. The impact of the changing wind farm distribution on the wind-power statistics is significant. The annual mean capacity factor increased from 32.7% for the current wind farm distribution to 39.7% for the future distribution. In addition, there are fewer periods of prolonged low generation and more periods of prolonged high generation. Finally, the frequency and magnitude of ramping in the nationally aggregated capacity factor remains largely unchanged. However, due to the increased capacity of the future distribution, in terms of power output, the magnitude of the ramping increases by a factor of 5

The importance of forecasting regional wind power ramping: a case study for the UK

In recent years there has been a significant change in the distribution of wind farms in Great Britain, with a trend towards very large offshore farms clustered together in zones. However, there are concerns these clusters could produce large ramping events on time scales of less than 6 hours as local meteorological phenomena simultaneously impact the production of several farms. This paper presents generation data from the wind farms in the Thames Estuary (the largest cluster in the world) for 2014 and quantifies the high frequency power ramps. Based on a case study of a ramping event which occurred on 3rd November 2014, we show that due to the large capacity of the cluster, a localised ramp can have a significant impact on the cost of balancing the power system on a national level if it is not captured by the forecast of the system operator. The planned construction of larger offshore wind zones will exacerbate this problem. Consequently, there is a need for accurate regional wind power forecasts to minimise the costs of managing the system. This study shows that state-of-the-art high resolution forecast models have capacity to provide valuable information to mitigate this impact

Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation

State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. The power system of Great Britain (GB) is used as an example because independent verifying data is available from National Grid. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the “resolved” uncertainty associated with estimating the future large-scale atmospheric state is larger than the “unresolved” uncertainty associated with estimating the system-wide wind power response to a given large-scale state. The bounds of skilful forecast range are quantified for three leading global forecast systems. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time are found to be 6-8 days. The lower bound is found to be 1.4-2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5-5.5 days). The upper bound to useful forecast range can only be extended by improving the global forecast system (outside the control of most users) or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound to useful forecast range. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days)

Pre-existing virus-specific CD8+ T-cells provide protection against pneumovirus-induced disease in mice

Pneumoviruses such as pneumonia virus of mice (PVM), bovine respiratory syncytial virus (bRSV) or human (h)RSV are closely related pneumoviruses that cause severe respiratory disease in their respective hosts. It is well-known that T-cell responses are essential in pneumovirus clearance, but pneumovirus-specific T-cell responses also are important mediators of severe immunopathology. In this study we determined whether memory- or pre-existing, transferred virus-specific CD8 + T-cells provide protection against PVM-induced disease. We show that during infection with a sublethal dose of PVM, both natural killer (NK) cells and CD8 + T-cells expand relatively late. Induction of CD8 + T-cell memory against a single CD8 + T-cell epitope, by dendritic cell (DC)-peptide immunization, leads to partial protection against PVM challenge and prevents Th2 differentiation of PVM-induced CD4 T-cells. In addition, adoptively transferred PVM-specific CD8 + T-cells, covering the entire PVM-specific CD8 + T-cell repertoire, provide partial protection from PVM-induced disease. From these data we infer that antigen-specific memory CD8 + T-cells offer significant protection to PVM-induced disease. Thus, CD8 + T-cells, despite being a major cause of PVM-associated pathology during primary infection, may offer promising targets of a protective pneumovirus vaccine

The Influence of Body Mass Index on Clinical Interpretation of Established and Novel Biomarkers in Acute Heart Failure

Background: Body mass index (BMI) is a known confounder for natriuretic peptides, but its influence on other biomarkers is less well described. We investigated whether BMI interacts with biomarkers’ association with prognosis in patients with acute heart failure (AHF).Methods and Results: B-type natriuretic peptide (BNP), high-sensitivity cardiac troponin I (hs-cTnI), galectin-3, serum neutrophil gelatinase-associated lipocalin (sNGAL), and urine NGAL were measured serially in patients with AHF during hospitalization in the AKINESIS (Acute Kidney Injury Neutrophil gelatinase-associated lipocalin Evaluation of Symptomatic Heart Failure) study. Cox regression analysis was used to determine the association of biomarkers and their interaction with BMI for 30-day, 90-day and 1-year composite outcomes of death or HF readmission. Among 866 patients, 21.2%, 29.7% and 46.8% had normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2) or obese (≥ 30 kg/m2) BMIs on admission, respectively. Admission values of BNP and hs-cTnI were negatively associated with BMI, whereas galectin-3 and sNGAL were positively associated with BMI. Admission BNP and hs-cTnI levels were associated with the composite outcome within 30 days, 90 days and 1 year. Only BNP had a significant interaction with BMI. When BNP was analyzed by BMI category, its association with the composite outcome attenuated at higher BMIs and was no longer significant in obese individuals. Findings were similar when evaluated by the last-measured biomarkers and BMIs.Conclusions: In patients with AHF, only BNP had a significant interaction with BMI for the outcomes, with its association attenuating as BMI increased; hs-cTnI was prognostic, regardless of BMI.</p

Short-term prognostic implications of serum and urine neutrophil gelatinase-associated lipocalin in acute heart failure:findings from the AKINESIS study

AIMS: Kidney impairment has been associated with worse outcomes in acute heart failure (AHF), although recent studies challenge this association. Neutrophil gelatinase-associated lipocalin (NGAL) is a novel biomarker of kidney tubular injury. Its prognostic role in AHF has not been evaluated in large cohorts. The present study aimed to determine if serum NGAL (sNGAL) or urine NGAL (uNGAL) is superior to creatinine for predicting short-term outcomes in AHF. METHODS AND RESULTS: The study was conducted in an international, multicentre, prospective cohort consisting of 927 patients with AHF. Admission and peak values of sNGAL, uNGAL and uNGAL/urine creatinine (uCr) ratio were compared to admission and peak serum creatinine (sCr). The composite endpoints were death, initiation of renal replacement therapy, heart failure (HF) readmission and any emergent HF-related outpatient visit within 30 and 60 days, respectively. The mean age of the cohort was 69 years and 62% were male. The median length of stay was 6 days. The composite endpoint occurred in 106 patients and 154 patients within 30 and 60 days, respectively. Serum NGAL was more predictive than uNGAL and the uNGAL/uCr ratio but was not superior to sCr (area under the curve [AUC]; admission sNGAL 0.61 [95% confidence interval (CI) 0.55-0.67] and 0.59 [95% CI 0.54-0.65], peak sNGAL 0.60 [95% CI 0.54-0.66] and 0.57 [95% CI 0.52-0.63], admission sCr 0.60 [95% CI 0.54-0.64] and 0.59 [95% CI 0.53-0.64] [area under the curve: admission sNGAL 0.61, 95% confidence interval (CI) 0.55-0.67, and 0.59, 95% CI 0.54-0.65; peak sNGAL: 0.60, 95% CI 0.54-0.66, and 0.57, 95% CI 0.52-0.63; admission sCr: 0.60, 95% CI 0.54-0.64, and 0.59, 95% CI 0.53-0.64, at 30 and 60 days, respectively], peak sCr 0.61 [95% CI 0.55-0.67] and 0.59 [95% CI 0.54-0.64] at 30 and 60 days, respectively). NGAL was not predictive of the composite endpoint in multivariate analysis. CONCLUSIONS: Serum NGAL outperformed uNGAL but neither was superior to admission or peak sCr for predicting adverse events

Potential Utility of Cardiorenal Biomarkers for Prediction and Prognostication of Worsening Renal Function in Acute Heart Failure

Background: Multiple different pathophysiologic processes can contribute to worsening renal function (WRF) in acute heart failure. Methods and Results: We retrospectively analyzed 787 patients with acute heart failure for the relationship between changes in serum creatinine and biomarkers including brain natriuretic peptide, high sensitivity cardiac troponin I, galectin 3, serum neutrophil gelatinase-associated lipocalin, and urine neutrophil gelatinase-associated lipocalin. WRF was defined as an increase of greater than or equal to 0.3 mg/dL or 50% in creatinine within first 5 days of hospitalization. WRF was observed in 25% of patients. Changes in biomarkers and creatinine were poorly correlated (r < 0.21) and no biomarker predicted WRF better than creatinine. In the multivariable Cox analysis, brain natriuretic peptide and high sensitivity cardiac troponin I, but not WRF, were significantly associated with the 1-year composite of death or heart failure hospitali-zation. WRF with an increasing urine neutrophil gelatinase-associated lipocalin predicted an increased risk of heart failure hospitalization. Conclusions: Biomarkers were not able to predict WRF better than creatinine. The 1-year outcomes were associated with biomarkers of cardiac stress and injury but not with WRF, whereas a kidney injury bio-marker may prognosticate WRF for heart failure hospitalization

Relation of Decongestion and Time to Diuretics to Biomarker Changes and Outcomes in Acute Heart Failure

Prompt treatment may mitigate the adverse effects of congestion in the early phase of heart failure (HF) hospitalization, which may lead to improved outcomes. We analyzed 814 acute HF patients for the relationships between time to first intravenous loop diuretics, changes in biomarkers of congestion and multiorgan dysfunction, and 1-year composite end point of death or HF hospitalization. B-type natriuretic peptide (BNP), high sensitivity cardiac troponin I (hscTnI), urine and serum neutrophil gelatinase-associated lipocalin, and galectin 3 were measured at hospital admission, hospital day 1, 2, 3 and discharge. Time to diuretics was not correlated with the timing of decongestion defined as BNP decrease >= 30% compared with admission. Earlier BNP decreases but not time to diuretics were associated with earlier and greater decreases in hscTnI and urine neutrophil gelatinase-associated lipocalin, and lower incidence of the composite end point. After adjustment for confounders, only no BNP decrease at discharge was significantly associated with mortality but not the composite end point (p = 0.006 and p = 0.062, respectively). In conclusion, earlier time to decongestion but not the time to diuretics was associated with better biomarker trajectories. Residual congestion at discharge rather than the timing of decongestion predicted a worse prognosis. (C) 2021 The Authors. Published by Elsevier Inc

Decongestion, kidney injury and prognosis in patients with acute heart failure

Background: In patients with acute heart failure (AHF), the development of worsening renal function with appropriate decongestion is thought to be a benign functional change and not associated with poor prognosis. We investigated whether the benefit of decongestion outweighs the risk of concurrent kidney tubular damage and leads to better outcomes.& nbsp;Methods: We retrospectively analyzed data from the AKINESIS study, which enrolled AHF patients requiring intravenous diuretic therapy. Urine neutrophil gelatinase-associated lipocalin (uNGAL) and B-type natriuretic peptide (BNP) were serially measured during the hospitalization. Decongestion was defined as >= 30% BNP decrease at discharge compared to admission. Univariable and multivariable Cox models were assessed for oneyear mortality.& nbsp;Results: Among 736 patients, 53% had >= 30% BNP decrease at discharge. Levels of uNGAL and BNP at each collection time point had positive but weak correlations (r = 30% BNP decrease was a significant predictor after multivariable adjustment.& nbsp;Conclusions: Among AHF patients treated with diuretic therapy, decongestion was generally not associated with kidney tubular damage assessed by uNGAL. Kidney tubular damage with adequate decongestion does not impact outcomes; however, kidney injury without adequate decongestion is associated with a worse prognosis