| 1. |
- Hop, Paul J., et al.
(författare)
-
Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS
- 2022
-
Ingår i: Science Translational Medicine. - : American Association for the Advancement of Science. - 1946-6234 .- 1946-6242. ; 14:633
-
Tidskriftsartikel (refereegranskat)abstract
- Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation-based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.
|
|
| 2. |
- Bombois, Xavier, et al.
(författare)
-
Optimal experiment design for hypothesis testing applied to functional magnetic resonance imaging
- 2011
-
Ingår i: Proceedings of the 18th IFAC World Congress. ; , s. 9953-9958
-
Konferensbidrag (refereegranskat)abstract
- Hypothesis testing is a classical methodology of making decisions using experimental data. In hypothesis testing one seeks to discover evidence that either accepts or rejects a given null hypothesis H0. The alternative hypothesis H1 is the hypothesis that is accepted when H0 is rejected. In hypothesis testing, the probability of deciding H1 when in fact H0 is true is known as the false alarm rate, whereas the probability of deciding H1when in fact H1is true is known as the detection rate (or power) of the test. It is not possible to optimize both rates simultaneously. In this paper, we consider the problem of determining the data to be used for hypothesis testing that maximize the detection rate for a given false alarm rate. We consider in particular a hypothesis test which is relevant in functional magnetic resonance imaging (fMRI).
|
|
| 3. |
- Heuberger, Peter S. C., et al.
(författare)
-
Orthonormal basis functions in time and frequency domain : Hambo transform theory
- 2004
-
Ingår i: SIAM Journal of Control and Optimization. - 0363-0129 .- 1095-7138. ; 42:4, s. 1347-1373
-
Tidskriftsartikel (refereegranskat)abstract
- The class of finite impulse response (FIR), Laguerre, and Kautz functions can be generalized to a family of rational orthonormal basis functions for the Hardy space H2 of stable linear dynamical systems. These basis functions are useful for constructing efficient parameterizations and coding of linear systems and signals, as required in, e.g., system identification, system approximation, and adaptive filtering. In this paper, the basis functions are derived from a transfer function perspective as well as in a state space setting. It is shown how this approach leads to alternative series expansions of systems and signals in time and frequency domain. The generalized basis functions induce signal and system transforms (Hambo transforms), which have proved to be useful analysis tools in various modelling problems. These transforms are analyzed in detail in this paper, and a large number of their properties are derived. Principally, it is shown how minimal state space realizations of the system transform can be obtained from minimal state space realizations of the original system and vice versa.
|
|
| 4. |
- Weerts, Harm H. M., et al.
(författare)
-
Abstractions of linear dynamic networks for input selection in local module identification
- 2020
-
Ingår i: Automatica. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0005-1098 .- 1873-2836. ; 117
-
Tidskriftsartikel (refereegranskat)abstract
- In abstractions of linear dynamic networks, selected node signals are removed from the network, while keeping the remaining node signals invariant. The topology and link dynamics, or modules, of an abstracted network will generally be changed compared to the original network. Abstractions of dynamic networks can be used to select an appropriate set of node signals that are to be measured, on the basis of which a particular local module can be estimated. A method is introduced for network abstraction that generalizes previously introduced algorithms, as e.g. immersion and the method of indirect inputs. For this abstraction method it is shown under which conditions on the selected signals a particular module will remain invariant. This leads to sets of conditions on selected measured node variables that allow identification of the target module. (C) 2020 Elsevier Ltd. All rights reserved.
|
|
| 5. |
- Özkan, Leyla, et al.
(författare)
-
Advanced autonomous model-based operation of industrial process systems (Autoprofit) : Technological developments and future perspectives
- 2016
-
Ingår i: ANNUAL REVIEWS IN CONTROL. - : Elsevier. - 1367-5788. ; 42, s. 126-142
-
Forskningsöversikt (refereegranskat)abstract
- Model-based operation support technology such as Model Predictive Control (MPC) is a proven and accepted technology for multivariable and constrained large scale control problems in process industry. Despite the growing number of successful implementations, the low level of operational efficiency of MPC is an existing problem, specifically the lack of advanced maintenance technology. To this end, within the EU FP 7 program, a project (Autoprofit (1)) has been executed to advance the level of autonomy and automated maintenance of MPC technology. Taking linear model-based technology as a starting point, in the project a philosophy has been developed for autonomous performance monitoring, diagnosis, experiment design, model adaptation and controller re-tuning, that is driven by economic criteria in each step, working towards an operation support system in which effective maintenance and adaptation of MPC controllers becomes feasible. In this development, challenging research questions have been addressed in the areas of on-line performance monitoring and diagnosis, least costly experiment design, automated adaptation of models, and auto-tuning, and new fundamental techniques have been developed. Although a full fledge and industrially proven (semi-)automated system is not yet realised, parts of the on-line system have been implemented and validated on real life cases provided by the industrial partners, showing that the formulated objectives are within reach.
|
|
| 6. |
- Bombois, Xavier, et al.
(författare)
-
On the informativity of direct identification experiments in dynamical networks
- 2023
-
Ingår i: Automatica. - : Elsevier BV. - 0005-1098 .- 1873-2836. ; 148, s. 110742-
-
Tidskriftsartikel (refereegranskat)abstract
- Data informativity is a crucial property to ensure the consistency of the prediction error estimate. This property has thus been extensively studied in the open-loop and in the closed-loop cases, but has only been briefly touched upon in the dynamic network case. In this paper, we consider the prediction error identification of the modules in a row of a dynamic network using the full input approach. Our main contribution is to propose a number of easily verifiable data informativity conditions for this identification problem. Among these conditions, we distinguish a sufficient data informativity condition that can be verified based on the topology of the network and a necessary and sufficient data informativity condition that can be verified via a rank condition on a matrix of coefficients that are related to a full-order model structure of the network. These data informativity conditions allow to determine different situations (i.e., different excitation patterns) leading to data informativity. In order to be able to distinguish between these different situations, we also propose an optimal experiment design problem that allows to determine the excitation pattern yielding a certain pre-specified accuracy with the least excitation power.
|
|
| 7. |
- Gevers, Michel, et al.
(författare)
-
Asymptotic Variance Expressions for Closed-Loop Identification and their Relevance in Identification for Control
- 1999
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Asymptotic variance expressions are analysed for models that are identified on the basis of closed-loop data. The considered methods comprise the classical "direct" and "indirect" method, as well as the more recently developed indirect methods, employing coprime factorized models and model parametrizations based on the dual Youla/Kucera parametrization. The variance expressions are compared with the open-loop situation, and evaluated in terms of their relevance for subsequent model-based control design. Additionally it is specified what is the optimal experimental situation in identification (open-loop or closed-loop), in view of the variance of the resulting model-based controller.
|
|
| 8. |
- Heuberger, Peter S.C., et al.
(författare)
-
Modelling and Identification with Rational Orthogonal Basis Functions
- 2005. - 1
-
Bok (refereegranskat)abstract
- Models of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science. A model is always only an approximation of a real phenomenon so that having an approximation theory which allows for the analysis of model quality is a substantial concern. The use of rational orthogonal basis functions to represent dynamical systems and stochastic signals can provide such a theory and underpin advanced analysis and efficient modelling. It also has the potential to extend beyond these areas to deal with many problems in circuit theory, telecommunications, systems, control theory and signal processing.Nine international experts have contributed to this work to produce thirteen chapters that can be read independently or as a comprehensive whole with a logical line of reasoning:Construction and analysis of generalized orthogonal basis function model structure;System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding;System identification in the frequency domain;Design issues and optimal basis selection;Transformation and realization theory.Modelling and Identification with Rational Orthogonal Basis Functions affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.
|
|
| 9. |
- Ljung, Lennart, 1946-, et al.
(författare)
-
Asymptotic Variance Expressions for Closed-Loop Identification and their Relevance in Identification for Control
- 1997
-
Ingår i: Proceedings of the 11th IFAC Symposium on Identification and System Parameter Estimation. ; , s. 1449-1455
-
Konferensbidrag (refereegranskat)abstract
- Asymptotic variance expressions are analysed for models that are identified on the basis of closed-loop data. The considered methods comprise the classical "direct" and "indirect" method, as well as the more recently developed indirect methods, employing coprime factorized models and model parametrizations based on the dual Youla/Kucera parametrization. The variance expressions are compared with the open-loop situation, and evaluated in terms of their relevance for subsequent model-based control design. Additionally it is specified what is the optimal experimental situation in identification (open-loop or closed-loop), in view of the variance of the resulting model-based controller.
|
|
| 10. |
- Lyzell, Christian, et al.
(författare)
-
Order and Structural Dependence Selection of LPV-ARX Models using a Nonnegative Garrote Approach
- 2009
-
Ingår i: Proceedings of the 48th IEEE Conference on Decision and Control held jointly with the 28th Chinese Control Conference. - 9781424438716 - 9781424438723 ; , s. 7406-7411
-
Konferensbidrag (refereegranskat)abstract
- In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.
|
|
