| 1. |
- Adlard, Bryan, et al.
(författare)
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MercuNorth–monitoring mercury in pregnant women from the Arctic as a baseline to assess the effectiveness of the Minamata Convention
- 2021
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Ingår i: International Journal of Circumpolar Health. - : Taylor & Francis. - 1239-9736 .- 2242-3982. ; 80:1
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Tidskriftsartikel (refereegranskat)abstract
- Exposure to mercury (Hg) is a global concern, particularly among Arctic populations that rely on the consumption of marine mammals and fish which are the main route of Hg exposure for Arctic populations.The MercuNorth project was created to establish baseline Hg levels across several Arctic regions during the period preceding the Minamata Convention. Blood samples were collected from 669 pregnant women, aged 18–44 years, between 2010 and 2016 from sites across the circumpolar Arctic including Alaska (USA), Nunavik (Canada), Greenland, Iceland, Norway, Sweden, Northern Lapland (Finland) and Murmansk Oblast (Russia). Descriptive statistics were calculated, multiple pairwise comparisons were made between regions, and unadjusted linear trend analyses were performed.Geometric mean concentrations of total Hg were highest in Nunavik (5.20 µg/L) and Greenland (3.79 µg/L), followed by Alaska (2.13 µg/L), with much lower concentrations observed in the other regions (ranged between 0.48 and 1.29 µg/L). In Nunavik, Alaska and Greenland, blood Hg concentrations have decreased significantly since 1992, 2000 and 2010 respectively with % annual decreases of 4.7%, 7.5% and 2.7%, respectively.These circumpolar data combined with fish and marine mammal consumption data can be used for assessing long-term Hg trends and the effectiveness of the Minamata Convention.
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| 2. |
- Brönnimann, Stefan, et al.
(författare)
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Unlocking Pre-1850 Instrumental Meteorological Records : A Global Inventory
- 2019
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Ingår i: Bulletin of The American Meteorological Society - (BAMS). - 0003-0007 .- 1520-0477. ; 100:12, s. ES389-ES413
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Tidskriftsartikel (refereegranskat)abstract
- Instrumental meteorological measurements from periods prior to the start of national weather services are designated early instrumental data. They have played an important role in climate research as they allow daily to decadal variability and changes of temperature, pressure, and precipitation, including extremes, to be addressed. Early instrumental data can also help place twenty-first century climatic changes into a historical context such as defining preindustrial climate and its variability. Until recently, the focus was on long, high-quality series, while the large number of shorter series (which together also cover long periods) received little to no attention. The shift in climate and climate impact research from mean climate characteristics toward weather variability and extremes, as well as the success of historical reanalyses that make use of short series, generates a need for locating and exploring further early instrumental measurements. However, information on early instrumental series has never been electronically compiled on a global scale. Here we attempt a worldwide compilation of metadata on early instrumental meteorological records prior to 1850 (1890 for Africa and the Arctic). Our global inventory comprises information on several thousand records, about half of which have not yet been digitized (not even as monthly means), and only approximately 20% of which have made it to global repositories. The inventory will help to prioritize data rescue efforts and can be used to analyze the potential feasibility of historical weather data products. The inventory will be maintained as a living document and is a first, critical, step toward the systematic rescue and reevaluation of these highly valuable early records. Additions to the inventory are welcome.
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| 3. |
- Korkalainen, Henri, et al.
(författare)
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Review and perspective on sleep-disordered breathing research and translation to clinics
- 2024
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Ingår i: SLEEP MEDICINE REVIEWS. - 1087-0792 .- 1532-2955. ; 73
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Forskningsöversikt (refereegranskat)abstract
- Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.
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| 4. |
- Leander, Jacob, 1987, et al.
(författare)
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Parameter Estimation for Nonlinear Mixed Effects Models Implemented in Mathematica
- 2019
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In many applications within biology and medicine, measurements are gathered from several entities in the same experiment. This could for example be patients exposed to a treatment or cells measured after stimuli. To characterize the variability in response between entities, the nonlinear mixed effects (NLME) model is a suitable statistical model. An NLME model enables quantification of both within- and between subject variability. The parameter estimation in NLME models is not straightforward, due to the intractable expression of the likelihood function. In this work we present a Mathematica package for parameter estimation in NLME models where the longitudinal model is defined by differential equations. The parameter estimation problem is solved by the first-order conditional estimation (FOCE) method with exact gradients. The package is demonstrated using data from a simulated drug concentration model.
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| 5. |
- Olafsdottir, Helga Kristin, et al.
(författare)
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Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics
- 2017
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. 44(Suppl 1): 11. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Nonlinear mixed effects (NLME) models based on stochastic differential equations (SDEs) have evolved into a mature approach for analysis of PKPD data [1-3], but parameter estimation remains challenging. We present an exact-gradient version of the first order conditional estimation (FOCE) method for SDE-NLME models, and investigate whether it enables faster estimation and better gradient precision/accuracy compared to finite difference gradients.
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| 6. |
- Ólafsdóttir, Helga Kristín, 1990, et al.
(författare)
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Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics
- 2018
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Ingår i: Aaps Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20:5
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Tidskriftsartikel (refereegranskat)abstract
- Nonlinear mixed effects (NLME) modeling based on stochastic differential equations (SDEs) have evolved into a promising approach for analysis of PK/PD data. SDE-NLME models go beyond the realm of standard population modeling as they consider stochastic dynamics, thereby introducing a probabilistic perspective on the state variables. This article presents a summary of the main contributions to SDE-NLME models found in the literature. The aims of this work were to develop an exact gradient version of the first-order conditional estimation (FOCE) method for SDE-NLME models and to investigate whether it enabled faster estimation and better gradient precision/accuracy compared to the use of gradients approximated by finite differences. A simulation-estimation study was set up whereby finite difference approximations of the gradients of each level were interchanged with the exact gradients. Following previous work, the uncertainty of the state variables was accounted for using the extended Kalman filter (EKF). The exact gradient FOCE method was implemented in Mathematica 11 and evaluated on SDE versions of three common PK/PD models. When finite difference gradients were replaced by exact gradients at both FOCE levels, relative runtimes improved between 6- and 32-fold, depending on model complexity. Additionally, gradient precision/accuracy was significantly better in the exact gradient case. We conclude that parameter estimation using FOCE with exact gradients can successfully be applied to SDE-NLME models.
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| 7. |
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| 8. |
- Ólafsdóttir, Helga Kristín, 1990, et al.
(författare)
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Extreme Rainfall Events in the Northeastern United States Become More Frequent with Rising Temperatures, but Their Intensity Distribution Remains Stable
- 2021
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Ingår i: Journal of Climate. - 0894-8755 .- 1520-0442. ; 34:22, s. 8863-8877
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Tidskriftsartikel (refereegranskat)abstract
- Both the intensities of individual extreme rainfall events and the frequency of such events are important for infrastructure planning. We develop a new statistical extreme value model, the PGEV model, which makes it possible to use high-quality annual maximum series data instead of less well-checked daily data to estimate trends in intensity and frequency separately. The method is applied to annual maximum data from Vol. 10 of NOAA Atlas 14, dating from approximately 1900 to 2014, showing that in the majority of 333 rain gauge stations in the northeastern United States the frequency of extreme rainfall events increases as mean temperature increases, but that there is little evidence of trends in the distribution of the intensities of individual extreme rainfall events. The median of the frequency trends corresponds to extreme rainfall becoming 83% more frequent for each 18C of temperature increase. Naturally, increasing trends in frequency also increase the yearly or decadal risks of very extreme rainfall events. Three other large areas in the contiguous United States, the Midwest, the Southeast, and Texas, are also studied, and show similar but weaker trends than those in the Northeast.
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| 9. |
- Ólafsdóttir, Helga Kristín, 1990, et al.
(författare)
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Locally tail-scale invariant scoring rules for evaluation of extreme value forecasts
- 2024
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Ingår i: International Journal of Forecasting. - 0169-2070. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- Statistical analysis of extremes can be used to predict the probability of future extreme events, such as large rainfalls or devastating windstorms. The quality of these forecasts can be measured through scoring rules. Locally scale invariant scoring rules give equal importance to the forecasts at different locations regardless of differences in the prediction uncertainty. This is a useful feature when computing average scores but can be an unnecessarily strict requirement when one is mostly concerned with extremes. We propose the concept of local weight-scale invariance, describing scoring rules fulfilling local scale invariance in a certain region of interest, and as a special case, local tail-scale invariance for large events. Moreover, a new version of the weighted continuous ranked probability score (wCRPS) called the scaled wCRPS (swCRPS) that possesses this property is developed and studied. The score is a suitable alternative for scoring extreme value models over areas with a varying scale of extreme events, and we derive explicit formulas of the score for the generalised extreme value distribution. The scoring rules are compared through simulations, and their usage is illustrated by modelling extreme water levels and annual maximum rainfall, and in an application to non-extreme forecasts for the prediction of air pollution.
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