| 1. |
- Campbell, Charles, et al.
(author)
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Bridging model and real catalysts: general discussion
- 2016
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In: Faraday Discussions. - 1359-6640 .- 1364-5498. ; 188, s. 565-589
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Journal article (other academic/artistic)abstract
- Charles Campbell opened the discussion of the paper by Hans-JoachimFreund: If you have a 3D gold particle and it spreads out to be a 2D particle whenyou adsorb CO2, it must gain energy stability. Did you estimate the energy changeof the overall process to do that?
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| 2. |
- Pinto, Dalila, et al.
(author)
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Convergence of Genes and Cellular Pathways Dysregulated in Autism Spectrum Disorders.
- 2014
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In: American journal of human genetics. - : Elsevier BV. - 1537-6605 .- 0002-9297. ; 94:5, s. 677-694
-
Journal article (peer-reviewed)abstract
- Rare copy-number variation (CNV) is an important source of risk for autism spectrum disorders (ASDs). We analyzed 2,446 ASD-affected families and confirmed an excess of genic deletions and duplications in affected versus control groups (1.41-fold, p = 1.0× 10(-5)) and an increase in affected subjects carrying exonic pathogenic CNVs overlapping known loci associated with dominant or X-linked ASD and intellectual disability (odds ratio = 12.62, p = 2.7× 10(-15), ∼3% of ASD subjects). Pathogenic CNVs, often showing variable expressivity, included rare de novo and inherited events at 36 loci, implicating ASD-associated genes (CHD2, HDAC4, and GDI1) previously linked to other neurodevelopmental disorders, as well as other genes such as SETD5, MIR137, and HDAC9. Consistent with hypothesized gender-specific modulators, females with ASD were more likely to have highly penetrant CNVs (p = 0.017) and were also overrepresented among subjects with fragile X syndrome protein targets (p = 0.02). Genes affected by de novo CNVs and/or loss-of-function single-nucleotide variants converged on networks related to neuronal signaling and development, synapse function, and chromatin regulation.
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| 3. |
- Almquist, Joachim, 1980, et al.
(author)
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Sensitivity Equations Provide More Robust Gradients and Faster Computation of the FOCE Approximation to the Population Likelihood
- 2015
-
In: Proceedings of the 24th Annual meeting of the Population Approach Group in Europe, PAGE2015.
-
Conference paper (other academic/artistic)abstract
- Objectives: The first order conditional estimation (FOCE) method [1] is still one of the parameter estimation workhorses for nonlinear mixed effects (NLME) modeling used in population pharmacokinetics and pharmacodynamics [2]. However, because this method involves two nested levels of optimizations, with respect to the empirical Bayes estimates and the population parameters, FOCE may be numerically unstable and have long run times, issues which are most apparent for models requiring numerical integration of differential equations. Methods: We propose an alternative implementation of the FOCE method, and the related FOCEI, for parameter estimation in NLME models [3]. Instead of obtaining the gradients needed for the two levels of quasi-Newton optimizations from the standard finite difference approximation, gradients are computed using so called sensitivity equations. Results: The advantages of the approach are demonstrated using different versions of a pharmacokinetic model defined by nonlinear differential equations. We show that both the accuracy and precision of gradients can be improved extensively, which will increase the chances of a successfully converging parameter estimation [4]. We also show that the proposed approach can lead to markedly reduced computational times. The accumulated effect of the novel gradient computations ranged from a 10-fold decrease in run times for the least complex model when comparing to forward finite differences, to a substantial 100-fold decrease for the most complex model when comparing to central finite differences. Conclusions: Considering the use of finite differences in for instance NONMEM and Phoenix NLME, our results suggests that signicant improvements in the execution of FOCE are possible and that the approach of sensitivity equations should be carefully considered for both levels of optimization. References: [1] Wang Y. Derivation of various NONMEM estimation methods. J of Pharmacokin Pharmacodyn (2007) 34(5): 575-593. [2] Johansson ÅM, Ueckert S, Plan EL, Hooker AC, Karlsson MO. Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7. J of Pharmacokin Pharmacodyn (2014) 41(3):223-238. [3] Almquist J, Leander J, Jirstrand M. Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood. In press J of Pharmacokin Pharmacodyn (2015). [4] Tapani S, Almquist J, Leander J, Ahlström C, Peletier LA, Jirstrand M, Gabrielsson J. Joint Feedback Analysis Modeling of Nonesterified Fatty Acids in Obese Zucker Rats and Normal Sprague–Dawley Rats after Different Routes of Administration of Nicotinic Acid. J Pharmaceutical Sciences (2014), 103(8):2571–2584.
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| 4. |
- Almquist, Joachim, 1980, et al.
(author)
-
Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood
- 2015
-
In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 42:3, s. 191-209
-
Journal article (peer-reviewed)abstract
- The first order conditional estimation (FOCE) method is still one of the parameter estimation workhorses for nonlinear mixed effects (NLME) modeling used in population pharmacokinetics and pharmacodynamics. However, because this method involves two nested levels of optimizations, with respect to the empirical Bayes estimates and the population parameters, FOCE may be numerically unstable and have long run times, issues which are most apparent for models requiring numerical integration of differential equations. We propose an alternative implementation of the FOCE method, and the related FOCEI, for parameter estimation in NLME models. Instead of obtaining the gradients needed for the two levels of quasi-Newton optimizations from the standard finite difference approximation, gradients are computed using so called sensitivity equations. The advantages of this approach were demonstrated using different versions of a pharmacokinetic model defined by nonlinear differential equations. We show that both the accuracy and precision of gradients can be improved extensively, which will increase the chances of a successfully converging parameter estimation. We also show that the proposed approach can lead to markedly reduced computational times. The accumulated effect of the novel gradient computations ranged from a 10-fold decrease in run times for the least complex model when comparing to forward finite differences, to a substantial 100-fold decrease for the most complex model when comparing to central finite differences. Considering the use of finite differences in for instance NONMEM and Phoenix NLME, our results suggests that significant improvements in the execution of FOCE are possible and that the approach of sensitivity equations should be carefully considered for both levels of optimization.
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| 5. |
- Anney, Richard, et al.
(author)
-
Individual common variants exert weak effects on the risk for autism spectrum disorders.
- 2012
-
In: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 21:21, s. 4781-92
-
Journal article (peer-reviewed)abstract
- While it is apparent that rare variation can play an important role in the genetic architecture of autism spectrum disorders (ASD), the contribution of common variation to ASD risk is less clear. To produce a more comprehensive picture, we report Stage 2 of the Autism Genome Project genome-wide association study, adding 1301 ASD families and bringing the total to 2705 families analysed (Stages 1 and 2). In addition to evaluating association of individual SNPs, we also sought evidence that common variants, en masse, might affect risk. Despite genotyping over a million SNPs covering the genome, no single SNP shows significant association with ASD or selected phenotypes at a genome-wide level. The SNP that achieves the smallest p-value from secondary analyses is rs1718101. It falls in CNTNAP2, a gene previously implicated in susceptibility for ASD. This SNP also shows modest association with age of word/phrase acquisition in ASD subjects, of interest because features of language development are also associated with other variation in CNTNAP2. By contrast, allele-scores derived from the transmission of common alleles to Stage 1 cases significantly predict case-status in the independent Stage 2 sample. Despite being significant, the variance explained by these allele scores was small (Vm< 1%). Based on results from individual SNPs and their en masse effect on risk, as inferred from the allele-score results, it is reasonable to conclude that common variants affect ASD risk but their individual effects are modest.
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| 6. |
- Casey, Jillian P, et al.
(author)
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A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder.
- 2012
-
In: Human Genetics. - : Springer Science and Business Media LLC. - 0340-6717 .- 1432-1203. ; 131:4, s. 565-579
-
Journal article (peer-reviewed)abstract
- Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data.
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| 7. |
- Elphinstone, Cassandra, et al.
(author)
-
Multiple Pleistocene refugia for Arctic Bell-Heather revealed with genomic analyses of modern and historic plants
- 2024
-
In: Journal of Biogeography. - 0305-0270 .- 1365-2699.
-
Journal article (peer-reviewed)abstract
- Aim: Arctic plants survived the Pleistocene glaciations in unglaciated refugia. The number, ages, and locations of these refugia are often unclear. We use high-resolution genomic data from present-day and Little-Ice-Age populations of Arctic Bell-Heather to re-evaluate the biogeography of this species and determine whether it had multiple independent refugia or a single refugium in Beringia. Location: Circumpolar Arctic and Coastal British Columbia (BC) alpine. Taxon: Cassiope tetragona L., subspecies saximontana and tetragona, outgroup C. mertensiana (Ericaceae). Methods: We built genotyping-by-sequencing (GBS) libraries using Cassiope tetragona tissue from 36 Arctic locations, including two ~250- to 500-year-old populations collected under glacial ice on Ellesmere Island, Canada. We assembled a de novo GBS reference to call variants. Population structure, genetic diversity and demography were inferred from PCA, ADMIXTURE, fastsimcoal2, SplitsTree, and several population genomics statistics. Results: Population structure analyses identified 4–5 clusters that align with geographic locations. Nucleotide diversity was highest in Beringia and decreased eastwards across Canada. Demographic coalescent analyses dated the following splits with Alaska: BC subspecies saximontana (5 mya), Russia (~1.4 mya), Europe (>200–600 kya), and Greenland (~60 kya). Northern Canada populations appear to have formed during the current interglacial (7–9 kya). Admixture analyses show genetic variants from Alaska appear more frequently in present-day than historic plants on Ellesmere Island. Conclusions: Population and demographic analyses support BC, Alaska, Russia, Europe and Greenland as all having had independent Pleistocene refugia. Northern Canadian populations appear to be founded during the current interglacial with genetic contributions from Alaska, Europe and Greenland. We found evidence, on Ellesmere Island, for continued recent gene flow in the last 250–500 years. These results suggest that a re-analysis of other Arctic species with shallow population structure using higher resolution genomic markers and demographic analyses may help reveal deeper structure and other circumpolar glacial refugia.
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| 8. |
- Holst, Birgitte, et al.
(author)
-
G Protein-Coupled Receptor 39 Deficiency Is Associated with Pancreatic Islet Dysfunction
- 2009
-
In: Endocrinology. - : The Endocrine Society. - 0013-7227 .- 1945-7170. ; 150, s. 2577-2585
-
Journal article (peer-reviewed)abstract
- G protein-coupled receptor (GPR)-39 is a seven-transmembrane receptor expressed mainly in endocrine and metabolic tissues that acts as a Zn++ sensor signaling mainly through the G(q) and G(12/13) pathways. The expression of GPR39 is regulated by hepatocyte nuclear factor (HNF)-1 alpha and HNF-4 alpha, and in the present study, we addressed the importance of GPR39 for glucose homeostasis and pancreatic islets function. The expression and localization of GPR39 were characterized in the endocrine pancreas and pancreatic cell lines. Gpr39(-/-) mice were studied in vivo, especially in respect of glucose tolerance and insulin sensitivity, and in vitro in respect of islet architecture, gene expression, and insulin secretion. Gpr39 was down-regulated on differentiation of the pluripotent pancreatic cell line AR42J cells toward the exocrine phenotype but was along with Pdx-1 strongly up-regulated on differentiation toward the endocrine phenotype. Immunohistochemistry demonstrated that GRP39 is localized selectively in the insulin-storing cells of the pancreatic islets as well as in the duct cells of the exocrine pancreas. Gpr39(-/-) mice displayed normal insulin sensitivity but moderately impaired glucose tolerance both during oral and iv glucose tolerance tests, and Gpr39(-/-) mice had decreased plasma insulin response to oral glucose. Islet architecture was normal in the Gpr39 null mice, but expression of Pdx-1 and Hnf-1 alpha was reduced. Isolated, perifused islets from Gpr39 null mice secreted less insulin in response to glucose stimulation than islets from wild-type littermates. It is concluded that GPR39 is involved in the control of endocrine pancreatic function, and it is suggested that this receptor could be a novel potential target for the treatment of diabetes. (Endocrinology 150: 2577-2585, 2009)
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| 9. |
- Leander, Jacob, 1987, et al.
(author)
-
Mixed-effects modeling using stochastic differential equations - Applications to pharmacokinetic modeling
- 2014
-
In: Population Approach Group Europe (PAGE) 2014.
-
Conference paper (other academic/artistic)abstract
- Objectives: The model dynamics is often assumed to be deterministic in traditional mixed-effects modeling. We want to extend the non-linear mixed-effects model to a so called stochastic differential mixed-effects model, to account for model deficiencies and uncertainty in the dynamics [1-4]. In extension to previous results, interactions between the output covariance and the random effects, together with correlation between random effects are considered. Moreover, we aim for a robust calculation of the gradient of the objective function by using sensitivity equations. Methods: The ordinary non-linear mixed-effects modeling framework is extended by considering stochastic differential equations. The population likelihood is approximated using Laplace's approximation together with the First Order Conditional Estimation with Interaction (FOCEI) method. The state variables of system (e.g., drug concentration) is estimated using the extended Kalman filter on an individual level. In contrast to the commonly used finite difference approximation of the gradient we utilize the so called sensitivity equations. These equations provide a robust and efficient evaluation of the objective function and its gradient. They are obtained by differentiating the update and prediction equations in the extended Kalman filter. Results: An algorithm for parameter estimation in stochastic differential mixed-effects models has been developed. It features sensitivity equations for a robust and efficient calculation of the gradient in both the outer and inner optimization problem. The stochastic differential mixed-effects framework is illustrated by using a pharmacokinetic model of nicotinic acid (NiAc) turnover in obese rats [5-7]. The analysis shows that the total error consists of pure measurement error together with a significant uncertainty in model dynamics. The smoothed state variables estimates are used to provide a visualization of uncertainty in variables after the parameter estimation has been completed. Conclusions: We account for three sources of variability by considering stochastic differential mixed-effects models. We are able to account for uncertainty in the dynamics, in addition to measurement noise and interindividual variability. The new model structure is able to handle interaction effects and correlation between random parameters. The uncertainty plots derived from smoothing serve as an illustrative way to understand output variability. References: [1] R. Overgaard, E.Jonsson, C. Tornøe, H. Madsen, Non-linear mixed-effects models with stochastic differential equations: Implementation of an estimation algorithm, Journal of Pharmacokinetics and Pharmacodynamics 32(1), 85-107 (2005) [2] S. Mortensen, S. Klim, B. Dammann, N. Kristensen, H. Madsen, R. Overgaard, A Matlab framework for estimation of NLME models using stochastic differential equations, Journal of Pharmacokinetics and Pharmacodynamics 34(5), 623-642 (2007) [3] M. Delattre, P. Del Moral, M. Lavielle - The SAEM algorithm in MONOLIX for Non-Linear Mixed Effects Models with Stochastic Differential Equations. PAGE 19 (2010) Abstract 1733 [4] M. Lavielle, M.Delattre - On the use of stochastic differential mixed effects models for modeling inter occasion variability. Models and methods. PAGE 21 (2012) Abstract2372 [5] C. Ahlström, L. Peletier, J. Gabrielsson, Challenges of a mechanistic feedback model describing nicotinic acid-induced changes in non-esterified fatty acids in rats. Journal of Pharmacokinetics and Pharmacodynamics 40(4), 497-512 (2013) [6] C. Ahlström, T. Kroon, L. Peletier, J. Gabrielsson, Feedback modeling of non-esterified fatty acids in obese Zucker rats after nicotinic acid infusions. Journal of Pharmacokinetics and Pharmacodynamics 40(6), 623-638 (2013) [7] C. Ahlström, Modelling of tolerance and rebound in normal diseased rats, Thesis for the degree of Doctor of Medicine, University of Gothenburg, 2011
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| 10. |
- Leander, Jacob, 1987, et al.
(author)
-
Mixed Effects Modeling Using Stochastic Differential Equations: Illustrated by Pharmacokinetic Data of Nicotinic Acid in Obese Zucker Rats
- 2015
-
In: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 17:3, s. 586-596
-
Journal article (peer-reviewed)abstract
- Inclusion of stochastic differential equations in mixed effects models provides means to quantify and distinguish three sources of variability in data. In addition to the two commonly encountered sources, measurement error and interindividual variability, we also consider uncertainty in the dynamical model itself. To this end, we extend the ordinary differential equation setting used in nonlinear mixed effects models to include stochastic differential equations. The approximate population likelihood is derived using the first-order conditional estimation with interaction method and extended Kalman filtering. To illustrate the application of the stochastic differential mixed effects model, two pharmacokinetic models are considered. First, we use a stochastic one-compartmental model with first-order input and nonlinear elimination to generate synthetic data in a simulated study. We show that by using the proposed method, the three sources of variability can be successfully separated. If the stochastic part is neglected, the parameter estimates become biased, and the measurement error variance is significantly overestimated. Second, we consider an extension to a stochastic pharmacokinetic model in a preclinical study of nicotinic acid kinetics in obese Zucker rats. The parameter estimates are compared between a deterministic and a stochastic NiAc disposition model, respectively. Discrepancies between model predictions and observations, previously described as measurement noise only, are now separated into a comparatively lower level of measurement noise and a significant uncertainty in model dynamics. These examples demonstrate that stochastic differential mixed effects models are useful tools for identifying incomplete or inaccurate model dynamics and for reducing potential bias in parameter estimates due to such model deficiencies.
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| 11. |
- Leander, Jacob, 1987, et al.
(author)
-
NLMEModeling: A Wolfram Mathematica Package for Nonlinear Mixed Effects Modeling of Dynamical Systems
- 2020
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Journal article (other academic/artistic)abstract
- Nonlinear mixed effects modeling is a powerful tool when analyzing data from several entities in an experiment. In this paper, we present NLMEModeling, a package for mixed effects modeling in Wolfram Mathematica. NLMEModeling supports mixed effects modeling of dynamical systems where the underlying dynamics are described by either ordinary or stochastic differential equations combined with a flexible observation error model. Moreover, NLMEModeling is a user-friendly package with functionality for model validation, visual predictive checks and simulation capabilities. The package is freely available and provides a flexible add-on to Wolfram Mathematica.
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| 12. |
- Leander, Jacob, 1987, et al.
(author)
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Nonlinear Mixed Effects Modeling of Deterministic and Stochastic Dynamical Systems in Wolfram Mathematica
- 2021
-
In: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963 .- 2405-8963. ; 54:7, s. 409-414
-
Conference paper (peer-reviewed)abstract
- Nonlinear mixed effects (NLME) modeling is a powerful tool to analyze timeseries data from several individual entities in an experiment. In this paper, we give a brief overview of a package for NLME modeling in Wolfram Mathematica entitled NLMEModeling, implementing the first-order conditional estimation method with sensitivity equation-based gradients for parameter estimation. NLMEModeling supports mixed effects modeling of dynamical systems where the underlying dynamics are described by either ordinary or stochastic differential equations combined with observation equations with flexible observation error models. Moreover, NLMEModeling is a user-friendly package with functionality for parameter estimation, model diagnostics (such as goodness-of-fit analysis and visual predictive checks), and model simulation. The package is freely available and provides an extensible add-on to Wolfram Mathematica.
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| 13. |
- Leander, Jacob, 1987, et al.
(author)
-
Parameter Estimation for Nonlinear Mixed Effects Models Implemented in Mathematica
- 2019
-
Conference paper (other academic/artistic)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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| 14. |
- Olafsdottir, Helga Kristin, et al.
(author)
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Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics
- 2017
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In: Journal of Pharmacokinetics and Pharmacodynamics. 44(Suppl 1): 11. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744.
-
Conference paper (other academic/artistic)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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| 15. |
- Ólafsdóttir, Helga Kristín, 1990, et al.
(author)
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Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics
- 2018
-
In: Aaps Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 20:5
-
Journal article (peer-reviewed)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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| 16. |
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| 17. |
- Patel, Riyaz S., et al.
(author)
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Subsequent Event Risk in Individuals With Established Coronary Heart Disease : Design and Rationale of the GENIUS-CHD Consortium
- 2019
-
In: Circulation. - 2574-8300. ; 12:4
-
Journal article (peer-reviewed)abstract
- BACKGROUND: The Genetics of Subsequent Coronary Heart Disease (GENIUS-CHD) consortium was established to facilitate discovery and validation of genetic variants and biomarkers for risk of subsequent CHD events, in individuals with established CHD.METHODS: The consortium currently includes 57 studies from 18 countries, recruiting 185 614 participants with either acute coronary syndrome, stable CHD, or a mixture of both at baseline. All studies collected biological samples and followed-up study participants prospectively for subsequent events.RESULTS: Enrollment into the individual studies took place between 1985 to present day with a duration of follow-up ranging from 9 months to 15 years. Within each study, participants with CHD are predominantly of self-reported European descent (38%-100%), mostly male (44%-91%) with mean ages at recruitment ranging from 40 to 75 years. Initial feasibility analyses, using a federated analysis approach, yielded expected associations between age (hazard ratio, 1.15; 95% CI, 1.14-1.16) per 5-year increase, male sex (hazard ratio, 1.17; 95% CI, 1.13-1.21) and smoking (hazard ratio, 1.43; 95% CI, 1.35-1.51) with risk of subsequent CHD death or myocardial infarction and differing associations with other individual and composite cardiovascular endpoints.CONCLUSIONS: GENIUS-CHD is a global collaboration seeking to elucidate genetic and nongenetic determinants of subsequent event risk in individuals with established CHD, to improve residual risk prediction and identify novel drug targets for secondary prevention. Initial analyses demonstrate the feasibility and reliability of a federated analysis approach. The consortium now plans to initiate and test novel hypotheses as well as supporting replication and validation analyses for other investigators.
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| 18. |
- Peacock, Mike, et al.
(author)
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Greenhouse gas emissions from urban ponds are driven by nutrient status and hydrology
- 2019
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In: Ecosphere. - : Wiley. - 2150-8925 .- 2150-8925. ; 10:3
-
Journal article (peer-reviewed)abstract
- Inland waters emit significant quantities of greenhouse gases (GHGs) such as methane (CH4) and carbon dioxide (CO2) to the atmosphere. On a global scale, these emissions are large enough that their contribution to climate change is now recognized by the Intergovernmental Panel on Climate Change. Much of the past focus on GHG emissions from inland waters has focused on lakes, reservoirs, and rivers, and the role of small, artificial waterbodies such as ponds has been overlooked. To investigate the spatial variation in GHG fluxes from artificial ponds, we conducted a synoptic survey of forty urban ponds in a Swedish city. We measured dissolved concentrations of CH4 and CO2, and made complementary measurements of water chemistry. We found that CH4 concentrations were greatest in high‐nutrient ponds (measured as total phosphorus and total organic carbon). For CO2, higher concentrations were associated with silicon and calcium, suggesting that groundwater inputs lead to elevated CO2. When converted to diffusive GHG fluxes, mean emissions were 30.3 mg CH4·m−2·d−1 and 752 mg CO2·m−2·d−1. Although these fluxes are moderately high on an areal basis, upscaling them to all Swedish urban ponds gives an emission of 8336 t CO2eq/yr (±1689) equivalent to 0.1% of Swedish agricultural GHG emissions. Artificial ponds could be important GHG sources in countries with larger proportions of urban land.
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| 19. |
- Pedersen, Mogens Theisen, et al.
(author)
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Effect of team sports and resistance training on physical function, quality of life, and motivation in older adults
- 2017
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In: Scandinavian Journal of Medicine and Science in Sports. - Chichester : Wiley-Blackwell. - 0905-7188 .- 1600-0838. ; 27:8, s. 852-864
-
Journal article (peer-reviewed)abstract
- The aim of this study was to investigate the effect of team sports and resistance training on physical function, psychological health, quality of life, and motivation in older untrained adults. Twenty‐five untrained men and forty‐seven untrained women aged 80 (range: 67‐93) years were recruited. Fifty‐one were assigned to a training group (TRG) of which twenty‐five performed team training (TG) and twenty‐six resistance training (RG). The remaining twenty‐one were allocated to a control group (CG). TRG trained for 1 hour twice a week for 12 weeks. Compared with CG, TRG improved the number of arm curls within 30 seconds (P<.05) and 30‐seconds chair stand (P<.05) during the intervention. In TRG, participation in training led to higher (P<.05) scores in the subscales psychological well‐being, general quality of life, and health‐related quality of life, as well as decreased anxiety and depression levels. No differences between changes in TG and RG were found over the intervention period, neither in physical function tests nor psychological questionnaires. Both TG and RG were highly motivated for training, but TG expressed a higher degree of enjoyment and intrinsic motivation mainly due to social interaction during the activity, whereas RG was more motivated by extrinsic factors like health and fitness benefits. In conclusion, both team training and resistance training improved physical function, psychological well‐being, and quality of life. However, team sport training motivated the participants more by intrinsic factors than resistance training. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
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| 20. |
- Robba, Chiara, et al.
(author)
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Oxygen targets and 6-month outcome after out of hospital cardiac arrest : a pre-planned sub-analysis of the targeted hypothermia versus targeted normothermia after Out-of-Hospital Cardiac Arrest (TTM2) trial
- 2022
-
In: Critical Care. - : Springer Science and Business Media LLC. - 1364-8535 .- 1466-609X. ; 26, s. 1-13
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Journal article (peer-reviewed)abstract
- Background: Optimal oxygen targets in patients resuscitated after cardiac arrest are uncertain. The primary aim of this study was to describe the values of partial pressure of oxygen values (PaO2) and the episodes of hypoxemia and hyperoxemia occurring within the first 72 h of mechanical ventilation in out of hospital cardiac arrest (OHCA) patients. The secondary aim was to evaluate the association of PaO2 with patients’ outcome. Methods: Preplanned secondary analysis of the targeted hypothermia versus targeted normothermia after OHCA (TTM2) trial. Arterial blood gases values were collected from randomization every 4 h for the first 32 h, and then, every 8 h until day 3. Hypoxemia was defined as PaO2 < 60 mmHg and severe hyperoxemia as PaO2 > 300 mmHg. Mortality and poor neurological outcome (defined according to modified Rankin scale) were collected at 6 months. Results: 1418 patients were included in the analysis. The mean age was 64 ± 14 years, and 292 patients (20.6%) were female. 24.9% of patients had at least one episode of hypoxemia, and 7.6% of patients had at least one episode of severe hyperoxemia. Both hypoxemia and hyperoxemia were independently associated with 6-month mortality, but not with poor neurological outcome. The best cutoff point associated with 6-month mortality for hypoxemia was 69 mmHg (Risk Ratio, RR = 1.009, 95% CI 0.93–1.09), and for hyperoxemia was 195 mmHg (RR = 1.006, 95% CI 0.95–1.06). The time exposure, i.e., the area under the curve (PaO2-AUC), for hyperoxemia was significantly associated with mortality (p = 0.003). Conclusions: In OHCA patients, both hypoxemia and hyperoxemia are associated with 6-months mortality, with an effect mediated by the timing exposure to high values of oxygen. Precise titration of oxygen levels should be considered in this group of patients. Trial registration: clinicaltrials.gov NCT02908308, Registered September 20, 2016.
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| 21. |
- Robba, Chiara, et al.
(author)
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Ventilatory settings in the initial 72 h and their association with outcome in out-of-hospital cardiac arrest patients : a preplanned secondary analysis of the targeted hypothermia versus targeted normothermia after out-of-hospital cardiac arrest (TTM2) trial
- 2022
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In: Intensive Care Medicine. - : Springer Science and Business Media LLC. - 0342-4642 .- 1432-1238. ; 48:8, s. 1024-1038
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Journal article (peer-reviewed)abstract
- Purpose: The optimal ventilatory settings in patients after cardiac arrest and their association with outcome remain unclear. The aim of this study was to describe the ventilatory settings applied in the first 72 h of mechanical ventilation in patients after out-of-hospital cardiac arrest and their association with 6-month outcomes. Methods: Preplanned sub-analysis of the Target Temperature Management-2 trial. Clinical outcomes were mortality and functional status (assessed by the Modified Rankin Scale) 6 months after randomization. Results: A total of 1848 patients were included (mean age 64 [Standard Deviation, SD = 14] years). At 6 months, 950 (51%) patients were alive and 898 (49%) were dead. Median tidal volume (VT) was 7 (Interquartile range, IQR = 6.2–8.5) mL per Predicted Body Weight (PBW), positive end expiratory pressure (PEEP) was 7 (IQR = 5–9) cmH20, plateau pressure was 20 cmH20 (IQR = 17–23), driving pressure was 12 cmH20 (IQR = 10–15), mechanical power 16.2 J/min (IQR = 12.1–21.8), ventilatory ratio was 1.27 (IQR = 1.04–1.6), and respiratory rate was 17 breaths/minute (IQR = 14–20). Median partial pressure of oxygen was 87 mmHg (IQR = 75–105), and partial pressure of carbon dioxide was 40.5 mmHg (IQR = 36–45.7). Respiratory rate, driving pressure, and mechanical power were independently associated with 6-month mortality (omnibus p-values for their non-linear trajectories: p < 0.0001, p = 0.026, and p = 0.029, respectively). Respiratory rate and driving pressure were also independently associated with poor neurological outcome (odds ratio, OR = 1.035, 95% confidence interval, CI = 1.003–1.068, p = 0.030, and OR = 1.005, 95% CI = 1.001–1.036, p = 0.048). A composite formula calculated as [(4*driving pressure) + respiratory rate] was independently associated with mortality and poor neurological outcome. Conclusions: Protective ventilation strategies are commonly applied in patients after cardiac arrest. Ventilator settings in the first 72 h after hospital admission, in particular driving pressure and respiratory rate, may influence 6-month outcomes.
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| 22. |
- Salek, Reza M, et al.
(author)
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COordination of Standards in MetabOlomicS (COSMOS) : facilitating integrated metabolomics data access
- 2015
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In: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 11:6, s. 1587-1597
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Journal article (peer-reviewed)abstract
- Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.
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| 23. |
- Tapani, Sofia, 1982, et al.
(author)
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Joint feedback analysis modeling of nonesterified fatty acids in obese zucker rats and normal sprague-dawley rats after different routes of administration of nicotinic acid
- 2014
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In: Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0022-3549 .- 1520-6017. ; 103:8, s. 2571-2584
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Journal article (peer-reviewed)abstract
- Data were pooled from several studies on nicotinic acid (NiAc) intervention of fatty acid turnover in normal Sprague-Dawley and obese Zucker rats in order to perform a joint PKPD of data from more than 100 normal Sprague-Dawley and obese Zucker rats, exposed to several administration routes and rates. To describe the difference in pharmacodynamic parameters between obese and normal rats, we modified a previously published nonlinear mixed effects model describing tolerance and oscillatory rebound effects of NiAc on nonesterified fatty acids plasma concentrations. An important conclusion is that planning of experiments and dose scheduling cannot rely on pilot studies on normal animals alone. The obese rats have a less-pronounced concentration-response relationship and need higher doses to exhibit desired response. The relative level of fatty acid rebound after cessation of NiAc administration was also quantified in the two rat populations. Building joint normal-disease models with scaling parameter(s) to characterize the "degree of disease" can be a useful tool when designing informative experiments on diseased animals, particularly in the preclinical screen. Data were analyzed using nonlinear mixed effects modeling, for the optimization, we used an improved method for calculating the gradient than the usually adopted finite difference approximation.
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| 24. |
- Via, Allegra, et al.
(author)
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Best practices in bioinformatics training for life scientists
- 2013
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In: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 14:5, s. 528-537
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Journal article (peer-reviewed)abstract
- The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists.
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| 25. |
- Voss, Maren, et al.
(author)
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Origin and fate of dissolved organic matter in four shallow Baltic Sea estuaries
- 2021
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In: Biogeochemistry. - : Springer Science and Business Media LLC. - 0168-2563 .- 1573-515X. ; 154:2, s. 385-403
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Journal article (peer-reviewed)abstract
- Coastal waters have strong gradients in dissolved organic matter (DOM) quantity and characteristics, originating from terrestrial inputs and autochthonous production. Enclosed seas with high freshwater input therefore experience high DOM concentrations and gradients from freshwater sources to more saline waters. The brackish Baltic Sea experiences such salinity gradients from east to west and from river mouths to the open sea. Furthermore, the catchment areas of the Baltic Sea are very diverse and vary from sparsely populated northern areas to densely populated southern zones. Coastal systems vary from enclosed or open bays, estuaries, fjords, archipelagos and lagoons where the residence time of DOM at these sites varies and may control the extent to which organic matter is biologically, chemically or physically modified or simply diluted with transport off-shore. Data of DOM with simultaneous measurements of dissolved organic (DO) nitrogen (N), carbon (C) and phosphorus (P) across a range of contrasting coastal systems are scarce. Here we present data from the Roskilde Fjord, Vistula and Öre estuaries and Curonian Lagoon; four coastal systems with large differences in salinity, nutrient concentrations, freshwater inflow and catchment characteristics. The C:N:P ratios of DOM of our data, despite high variability, show site specific significant differences resulting largely from differences residence time. Microbial processes seemed to have minor effects, and only in spring did uptake of DON in the Vistula and Öre estuaries take place and not at the other sites or seasons. Resuspension from sediments impacts bottom waters and the entire shallow water column in the Curonian Lagoon. Finally, our data combined with published data show that land use in the catchments seems to impact the DOC:DON and DOC:DOP ratios of the tributaries most.
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| 26. |
- Warszawski, Lila, et al.
(author)
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All options, not silver bullets, needed to limit global warming to 1.5 °C : a scenario appraisal
- 2021
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In: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 16:6
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Journal article (peer-reviewed)abstract
- Climate science provides strong evidence of the necessity of limiting global warming to 1.5 °C, in line with the Paris Climate Agreement. The IPCC 1.5 °C special report (SR1.5) presents 414 emissions scenarios modelled for the report, of which around 50 are classified as '1.5 °C scenarios', with no or low temperature overshoot. These emission scenarios differ in their reliance on individual mitigation levers, including reduction of global energy demand, decarbonisation of energy production, development of land-management systems, and the pace and scale of deploying carbon dioxide removal (CDR) technologies. The reliance of 1.5 °C scenarios on these levers needs to be critically assessed in light of the potentials of the relevant technologies and roll-out plans. We use a set of five parameters to bundle and characterise the mitigation levers employed in the SR1.5 1.5 °C scenarios. For each of these levers, we draw on the literature to define 'medium' and 'high' upper bounds that delineate between their 'reasonable', 'challenging' and 'speculative' use by mid century. We do not find any 1.5 °C scenarios that stay within all medium upper bounds on the five mitigation levers. Scenarios most frequently 'over use' CDR with geological storage as a mitigation lever, whilst reductions of energy demand and carbon intensity of energy production are 'over used' less frequently. If we allow mitigation levers to be employed up to our high upper bounds, we are left with 22 of the SR1.5 1.5 °C scenarios with no or low overshoot. The scenarios that fulfil these criteria are characterised by greater coverage of the available mitigation levers than those scenarios that exceed at least one of the high upper bounds. When excluding the two scenarios that exceed the SR1.5 carbon budget for limiting global warming to 1.5 °C, this subset of 1.5 °C scenarios shows a range of 15–22 Gt CO2 (16–22 Gt CO2 interquartile range) for emissions in 2030. For the year of reaching net zero CO2 emissions the range is 2039–2061 (2049–2057 interquartile range).
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| 27. |
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| 28. |
- Weyhenmeyer, Gesa A., et al.
(author)
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Towards critical white ice conditions in lakes under global warming.
- 2022
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- The quality of lake ice is of uppermost importance for ice safety and under-ice ecology, but its temporal and spatial variability is largely unknown. Here we conducted a coordinated lake ice quality sampling campaign across the Northern Hemisphere during one of the warmest winters since 1880 and show that lake ice during 2020/2021 commonly consisted of unstable white ice, at times contributing up to 100% to the total ice thickness. We observed that white ice increased over the winter season, becoming thickest and constituting the largest proportion of the ice layer towards the end of the ice cover season when fatal winter drownings occur most often and light limits the growth and reproduction of primary producers. We attribute the dominance of white ice before ice-off to air temperatures varying around the freezing point, a condition which occurs more frequently during warmer winters. Thus, under continued global warming, the prevalence of white ice is likely to substantially increase during the critical period before ice-off, for which we adjusted commonly used equations for human ice safety and light transmittance through ice.
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| 29. |
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