| 21. |
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| 22. |
- Kaufmann, Tobias, et al.
(författare)
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Common brain disorders are associated with heritable patterns of apparent aging of the brain
- 2019
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Ingår i: Nature Neuroscience. - : Nature Publishing Group. - 1097-6256 .- 1546-1726. ; 22:10, s. 1617-
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Tidskriftsartikel (refereegranskat)abstract
- Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.
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| 23. |
- Lalani, Tahaniyat, et al.
(författare)
-
Propionibacterium endocarditis: a case series from the International Collaboration on Endocarditis Merged Database and Prospective Cohort Study.
- 2007
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Ingår i: Scandinavian journal of infectious diseases. - : Informa UK Limited. - 0036-5548 .- 1651-1980. ; 39:10, s. 840-8
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Tidskriftsartikel (refereegranskat)abstract
- Propionibacterium species are occasionally associated with serious systemic infections such as infective endocarditis. In this study, we examined the clinical features, complications and outcome of 15 patients with Propionibacterium endocarditis using the International Collaboration on Endocarditis Merged Database (ICE-MD) and Prospective Cohort Study (ICE-PCS), and compared the results to 28 cases previously reported in the literature. In the ICE database, 11 of 15 patients were male with a mean age of 52 y. Prosthetic valve endocarditis occurred in 13 of 15 cases and 3 patients had a history of congenital heart disease. Clinical findings included valvular vegetations (9 patients), cardiac abscesses (3 patients), congestive heart failure (2 patients), and central nervous system emboli (2 patients). Most patients were treated with beta-lactam antibiotics alone or in combination for 4 to 6 weeks. 10 of the 15 patients underwent valve replacement surgery and 2 patients died. Similar findings were noted on review of the literature. The results of this paper suggest that risk factors for Propionibacterium endocarditis include male gender, presence of prosthetic valves and congenital heart disease. The clinical course is characterized by complications such as valvular dehiscence, cardiac abscesses and congestive heart failure. Treatment may require a combination of medical and surgical therapy.
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| 24. |
- Lindberg, Oskar, et al.
(författare)
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Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden : Trading and forecast verification
- 2023
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Ingår i: Advances in Applied Energy. - : Elsevier. - 2666-7924. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.
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| 25. |
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| 26. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model
- 2019
-
Ingår i: Solar Energy. - : Elsevier BV. - 0038-092X .- 1471-1257. ; 184, s. 688-695
-
Tidskriftsartikel (refereegranskat)abstract
- This study presents a Markov-chain mixture (MCM) distribution model for forecasting the clear-sky index-normalized global horizontal irradiance. The model is presented in general, but applied to, and tested or minute resolution clear-sky index data for the two different climatic regions of Norrkoping, Sweden, and Hawaii USA. Model robustness is evaluated based on a cross-validation procedure and on that basis a reference con figuration of parameter settings for evaluating the model performance is obtained. Simulation results ar compared with persistence ensemble (PeEn) and quantile regression (QR) model simulations for both data set and for D = 1,...,5 steps ahead forecasting scenarios. The results are evaluated by a set of probabilistic fore casting metrics: reliability mean absolute error (reliability MAE), prediction interval normalized average widti (PINAW), continuous ranked probability score (CRPS) and continuous ranked probability skill score (skill). Botl in terms of reliability MAE and CRPS, the MCM model outperforms PeEn for all simulated scenarios. In terms c reliability MAE, the QR model outperforms the MCM model for most simulated scenarios. However, in terms c mean CRPS, the MCM model outperforms the QR model in most simulated scenarios. A point forecasting esti mate is also provided. The MCM model is concluded to be a computationally inexpensive, accurate and pars meter insensitive probabilistic model. Based on this, it is suggested as a candidate benchmark model in prop abilistic forecasting, in particular for solar irradiance forecasting. For applicability, a Python script of the MCA model is available as SheperoMah/MCM-distribution-forecasting at GitHub.
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| 27. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
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Probabilistic forecasting of the clear-sky index using Markov-chain mixture distribution and copula models
- 2019
-
Ingår i: 2019 Ieee 46Th Photovoltaic Specialists Conference (PVSC). - New York : IEEE. - 9781728104942 ; , s. 2428-2433
-
Konferensbidrag (refereegranskat)abstract
- Two probabilistic forecasting models for the clear-sky index, based on the Markov-chain mixture distribution (MCM) and copula clear-sky index generators, are presented and evaluated. In terms of performance, these models are compared with two benchmark models: a Quantile Regression (QR) model and the Persistence Ensemble (PeEn). The models are tested on minute resolution clear-sky index data, which was estimated from irradiance data for two different climatic regions: Hawaii, USA and Norrkoping, Sweden. Results show that the copula model generally outperforms the PeEn, while the MCM and QR models are superior in all tested aspects. Comparing MCM and QR reliability, the QR is superior, while the MCM is superior in mean CRPS and skill score. The MCM model is proposed as a potential benchmark for probabilistic solar forecasting. The MCM model is available in Python as SheperoMah/MCM-distribution-forecasting at GitHub.
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| 28. |
- Munkhammar, Joakim, 1982-, et al.
(författare)
-
Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model
- 2021
-
Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 282
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Tidskriftsartikel (refereegranskat)abstract
- This study utilizes the Markov-chain mixture distribution model (MCM) for very short term load forecasting of residential electricity consumption. The model is used to forecast one step ahead half hour resolution residential electricity consumption data from Australia. The results are compared with Quantile Regression (QR) and Persistence Ensemble (PeEn) as advanced and simple benchmark models. The results were compared in terms of reliability, reliability mean absolute error (rMAE), prediction interval normalized average width (PINAW) and normalized continuous ranked probability score (nCRPS). For 10 steps conditioning for QR and PeEn, the MCM results were on par with QR, and superior to PeEn. As a sensitivity analysis, simulations were performed where the number of data points for conditioning QR and PeEn was varied and compared to the MCM output, which is based on only one data point for conditioning. It was shown that in terms of nCRPS and rMAE the QR results converged towards the MCM results for lower number of conditioning points included in QR. The nCRPS of PeEn never reached the superior MCM and QR results, but in rMAE, for number of conditioning points above 24, PeEn was the most reliable. Based on the sparse complexity design of MCM, high computational speed and competitive performance, it is suggested as a candidate for benchmark model in probabilistic forecasting of electricity consumption.
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| 29. |
- Schindler, Louise S., et al.
(författare)
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Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women
- 2022
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Ingår i: NeuroImage. - : ELSEVIER SCI LTD. - 2213-1582. ; 36
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Tidskriftsartikel (refereegranskat)abstract
- The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.
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| 30. |
- Shepero, Mahmoud, 1992-, et al.
(författare)
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Residential probabilistic load forecasting : A method using Gaussian process designed for electric load data
- 2018
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 218, s. 159-172
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Tidskriftsartikel (refereegranskat)abstract
- Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data.
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