On Reduced Input-Output Dynamic Mode Decomposition

The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source. In this work, we consider the input-output dynamic mode decomposition method for system identification. We compare excitation approaches for the data-driven identification process and describe an optimization-based stabilization strategy for the identified systems

Model Order Reduction for Gas and Energy Networks

To counter the volatile nature of renewable energy sources, gas networks take a vital role. But, to ensure fulfillment of contracts under these circumstances, a vast number of possible scenarios, incorporating uncertain supply and demand, has to be simulated ahead of time. This many-query gas network simulation task can be accelerated by model reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic) equation systems, modeling natural gas transport, are a challenging application for model order reduction algorithms. For this industrial application, we bring together the scientific computing topics of: mathematical modeling of gas transport networks, numerical simulation of hyperbolic partial differential equation, and parametric model reduction for nonlinear systems. This research resulted in the "morgen" (Model Order Reduction for Gas and Energy Networks) software platform, which enables modular testing of various combinations of models, solvers, and model reduction methods. In this work we present the theoretical background on systemic modeling and structured, data-driven, system-theoretic model reduction for gas networks, as well as the implementation of "morgen" and associated numerical experiments testing model reduction adapted to gas network models

FINEMAP : a statistical method for identifying causal genetic variants

The explosion of genomic data during the last ten years and the advent of Genome-Wide Association Studies (GWAS) have led to robust statistical associations between thousands of genomic regions and hundreds of phenotypes. However, any one associated genomic region can harbor thousands of correlated genetic variants, complicating the understanding of the underlying biological mechanisms that led to these associations. To address this problem, this doctoral thesis presents the development of the FINEMAP software for fine-mapping causal variants in these regions. In 2016, we solved the existing issue with the computationally expensive exhaustive search strategy of existing fine-mapping methods by implementing a Bayesian regression model and an ultrafast stochastic search algorithm in the FINEMAP software. We demonstrated that FINEMAP opens up completely new opportunities by fine-mapping the High Density Lipoprotein (HDL) cholesterol association to the LIPC locus with 20,000 variants in less than 90 seconds, while exhaustive search would require many years. With extensive simulations we further showed that FINEMAP is as accurate as exhaustive search when the latter can be completed and achieves even higher accuracy when the latter must be restricted due to computational reasons. Thus, FINEMAP is a promising tool for future fine-mapping analyses. Fine-mapping methods that use GWAS results also require Linkage Disequilibrium (LD) information as input in the form of estimates of pairwise correlations between variants. Motivated by feedback from FINEMAP users, we investigated in 2017 the consequences of misspecification of LD that could happen when publicly available reference genomes are used. We demonstrated both empirically and theoretically that the size of the reference panel needs to scale with the GWAS sample size to produce accurate results and we provided the LDstore software to help share LD estimates. This finding has important consequences for the application of all fine-mapping methods using GWAS results from GW AS consortia in which accurate LD estimates from each participating study are typically not available. In 2018, we implemented in FINEMAP an approach for estimating how much phenotypic variation can be explained by the causal variants. To demonstrate this, we applied FINEMAP to 110 regions across 51 biomarkers on 5,265 Finnish samples. We compared regional heritability estimation using FINEMAP with both the variance component model BOLT and fixed-effect model HESS in biomarker-associated regions, showing good concordance among all methods. Through simulations with biobank-scale projects, we also illustrated how violations of model assumptions on polygenicity or unspecified genetic architecture induces inaccuracy to the existing heritability estimates that becomes more accentuated as statistical power to identify causal variants increases. Ever increasing GWAS sample sizes, soon reaching millions of samples, provide unprecedented statistical power to decompose heritability estimates from polygenic models into heritability contributions from causal variants. In conclusion, this doctoral thesis shows that (1) the computational efficiency and accuracy of FINEMAP makes it a promising fine-mapping tool, (2) LD estimates need to be chosen more carefully than previously thought to avoid bias, and (3) large-scale data sets provide new opportunities for fine-mapping to deduce a variant-level picture of regional genetic architecture

biMM : efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements

Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals.Peer reviewe

Hand-Assisted laparoscopic donor nephrectomy PERiumbilical versus Pfannenstiel incision and return to normal physical ACTivity (HAPERPACT): study protocol for a randomized controlled trial

Background: Hand-assisted laparoscopic living donor nephrectomy (HALDN) using a periumbilical or Pfannenstiel incision was developed to improve donor outcome after a kidney transplant. The aim of this study was to investigate two methods of hand assistance and kidney removal during HALDN and their effect on the time it takes for the donor to return to normal physical activity. Methods/design: This study was initiated in November 2017 and is expected to last for 2 years. To be eligible for the study, donors must be more than 20 years of age and must not be receiving permanent pain therapy. Only donors with a single artery and vein in the graft are being enrolled in this trial. Donors with infections or scars in the periumbilical or hypogastric area, bleeding disorders, chronic use of immunosuppressive agents, or active infection will be excluded. Donors will be randomly allocated to either a control arm (periumbilical incision) or an intervention arm (Pfannenstiel incision). The sample size was calculated as 26 organ donors in each group. The primary endpoint is the number of days it takes the donor to return to normal physical activity (up to 4 weeks after the operation). Secondary endpoints are intraoperative outcomes, including estimated blood loss, warm ischemia time, and duration of the operation. Postoperative pain will be assessed using the visual analog scale, rescue analgesic use, and peak expiratory flow rate. Length of hospital stay, physical activity score, time to return to work, donor satisfaction, cosmetic score, postoperative complications, and all-cause mortality in living donors will also be reported. Delayed graft function, primary non-function, serum creatinine levels, and glomerular filtration rate will also be assessed in the recipients after transplantation. Discussion: This is the first randomized controlled trial to compare the time it takes the living donor to return to normal physical activity after HALDN using two different types of incision. The comprehensive findings of this study will help decide which nephrectomy procedure is best for living donors with regard to patient comfort and satisfaction as well as graft function in the recipient after transplantation. Trial registration: ClinicalTrials.gov, NCT03317184 . Registered on 23 October 2017

FINEMAP : efficient variable selection using summary data from genome-wide association studies

Motivation: The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive. Results: We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing projects.Peer reviewe

Major Depressive Disorder is Associated with Impaired Mitochondrial Function in Skin Fibroblasts

Mitochondrial malfunction is supposed to be involved in the etiology and pathology of major depressive disorder (MDD). Here, we aimed to identify and characterize the molecular pathomechanisms related to mitochondrial dysfunction in adult human skin fibroblasts, which were derived from MDD patients or non-depressive control subjects. We found that MDD fibroblasts showed significantly impaired mitochondrial functioning: basal and maximal respiration, spare respiratory capacity, non-mitochondrial respiration and adenosine triphosphate (ATP)-related oxygen consumption was lower. Moreover, MDD fibroblasts harbor lower ATP levels and showed hyperpolarized mitochondrial membrane potential. To investigate cellular resilience, we challenged both groups of fibroblasts with hormonal (dexamethasone) or metabolic (galactose) stress for one week, and found that both stressors increased oxygen consumption but lowered ATP content in MDD as well as in non-depressive control fibroblasts. Interestingly, the bioenergetic differences between fibroblasts from MDD or non-depressed subjects, which were observed under non-treated conditions, could not be detected after stress. Our findings support the hypothesis that altered mitochondrial function causes a bioenergetic imbalance, which is associated with the molecular pathophysiology of MDD. The observed alterations in the oxidative phosphorylation system (OXPHOS) and other mitochondria-related properties represent a basis for further investigations of pathophysiological mechanisms and might open new ways to gain insight into antidepressant signaling pathways