| 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
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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. |
- Abend, Sven, et al.
(author)
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Terrestrial very-long-baseline atom interferometry : Workshop summary
- 2024
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In: AVS Quantum Science. - : American Institute of Physics (AIP). - 2639-0213. ; 6:2
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Research review (peer-reviewed)abstract
- This document presents a summary of the 2023 Terrestrial Very-Long-Baseline Atom Interferometry Workshop hosted by CERN. The workshop brought together experts from around the world to discuss the exciting developments in large-scale atom interferometer (AI) prototypes and their potential for detecting ultralight dark matter and gravitational waves. The primary objective of the workshop was to lay the groundwork for an international TVLBAI proto-collaboration. This collaboration aims to unite researchers from different institutions to strategize and secure funding for terrestrial large-scale AI projects. The ultimate goal is to create a roadmap detailing the design and technology choices for one or more kilometer--scale detectors, which will be operational in the mid-2030s. The key sections of this report present the physics case and technical challenges, together with a comprehensive overview of the discussions at the workshop together with the main conclusions.
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| 4. |
- 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
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In: Proceedings of the 24th Annual meeting of the Population Approach Group in Europe, PAGE2015.
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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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| 5. |
- Almquist, Joachim, 1980, et al.
(author)
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Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood
- 2015
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In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 42:3, s. 191-209
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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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| 6. |
- Anney, Richard, et al.
(author)
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Individual common variants exert weak effects on the risk for autism spectrum disorders.
- 2012
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In: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 21:21, s. 4781-92
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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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| 7. |
- 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
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In: Human Genetics. - : Springer Science and Business Media LLC. - 0340-6717 .- 1432-1203. ; 131:4, s. 565-579
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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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| 8. |
- Elphinstone, Cassandra, et al.
(author)
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Multiple Pleistocene refugia for Arctic Bell-Heather revealed with genomic analyses of modern and historic plants
- 2024
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In: Journal of Biogeography. - 0305-0270 .- 1365-2699.
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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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| 9. |
- Holst, Birgitte, et al.
(author)
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G Protein-Coupled Receptor 39 Deficiency Is Associated with Pancreatic Islet Dysfunction
- 2009
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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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| 10. |
- Leander, Jacob, 1987, et al.
(author)
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Mixed-effects modeling using stochastic differential equations - Applications to pharmacokinetic modeling
- 2014
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In: Population Approach Group Europe (PAGE) 2014.
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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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