Identifying and exchanging good practises of professional (re)integration of young and elderly people (45+) in a participatory bottom-up approach: experiences and findings from a research formation project

Scientific Conference (2011)

Identifying and targeting metabolic vulnerabilities of IDH mutant gliomas

Doctoral thesis (2021)

Diffuse gliomas are a group of central nervous system (CNS) tumors with a poor patient prognosis. Within these diffuse gliomas, isocitrate dehydrogenase (IDH) mutation defines the different tumor subtypes ... [more ▼]

Diffuse gliomas are a group of central nervous system (CNS) tumors with a poor patient prognosis. Within these diffuse gliomas, isocitrate dehydrogenase (IDH) mutation defines the different tumor subtypes and is considered to be an initiating event in gliomagenesis. IDH is a metabolic enzyme that in normal conditions mediates the conversion of isocitrate into α-ketoglutarate (α-KG), producing the reducing equivalent NADPH. IDH mutation (IDHm) leads to a neomorphic reaction where α-KG is consumed to generate the oncometabolite D-2-hydroxyglutarate (D-2HG), using NADPH as reducing agent. It has been reported that IDHm-dependent D-2HG synthesis has a direct impact on DNA and histone methylation, however the metabolic repercussions are not yet well defined. Due to the consumption of NADPH by IDHm reaction, some groups including us have hypothesized that IDHm cells may bear an imbalance of reducing equivalents, that may trigger a defective antioxidant defense. In the present study we made use of patient-derived cell lines and xenografts thereof as well as clinical samples in order to study the metabolic vulnerabilities of IDHm gliomas. In the first part of the thesis experimental data, we generated an integrative liquid chromatography-mass spectrometry (LCMS)-based proteomic-metabolomic characterization of IDHm metabolism. We made use of patients, cell lines and xenografts to address the direct effect of the mutation. We observed that IDHm gliomas have altered regulation of key processes in central carbon metabolism through glucose and glutamate processing as well as glutathione (GSH) metabolism and fatty acid production. In the second part of experimental data, we investigated the redox vulnerabilities of IDHm gliomas. Here we discovered that IDHm astrocytomas specifically upregulate cystathionine-γ-lyase (CSE) enabling them to synthesize GSH independently of NADPH. CSE is the only known enzyme capable of synthesizing cysteine. We found that genetic and chemical inhibition of CSE led to a decrease in cell viability upon cysteine restriction. Finally inhibition of CSE in vivo led to a delay in tumor growth rate. In conclusion, in the present PhD dissertation we expose a comprehensive study of the metabolic behavior of IDHm human gliomas, and we propose a novel therapeutic strategy that might improve patient prognosis, by inflicting oxidative damage to the tumor. [less ▲]

Identifying Attribute Importance in Early Product Development. Exemplified by Interactive Technologies and Age

Doctoral thesis (2011)

Identifying elastoplastic parameters with Bayes' theorem considering double error sources and model uncertainty

in Probabilistic Engineering Mechanics (2019), 55

We discuss Bayesian inference for the identi cation of elastoplastic material parameters. In addition to errors in the stress measurements, which are commonly considered, we furthermore consider errors in ... [more ▼]

We discuss Bayesian inference for the identi cation of elastoplastic material parameters. In addition to errors in the stress measurements, which are commonly considered, we furthermore consider errors in the strain measurements. Since a difference between the model and the experimental data may still be present if the data is not contaminated by noise, we also incorporate the possible error of the model itself. The three formulations to describe model uncertainty in this contribution are: (1) a random variable which is taken from a normal distribution with constant parameters, (2) a random variable which is taken from a normal distribution with an input-dependent mean, and (3) a Gaussian random process with a stationary covariance function. Our results show that incorporating model uncertainty often, but not always, improves the results. If the error in the strain is considered as well, the results improve even more. [less ▲]

Identifying fibre material parameter distributions with little experimental efforts

Scientific Conference (2018, July 23)

Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations

in Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017) (2017)

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging ... [more ▼]

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection systems are still largely based on expert knowledge when deciding whether to carry out costly on-site inspections of customers. Electricity providers are reluctant to move to large-scale deployments of automated systems that learn NTL profiles from data due to the latter's propensity to suggest a large number of unnecessary inspections. In this paper, we propose a novel system that combines automated statistical decision making with expert knowledge. First, we propose a machine learning framework that classifies customers into NTL or non-NTL using a variety of features derived from the customers' consumption data. The methodology used is specifically tailored to the level of noise in the data. Second, in order to allow human experts to feed their knowledge in the decision loop, we propose a method for visualizing prediction results at various granularity levels in a spatial hologram. Our approach allows domain experts to put the classification results into the context of the data and to incorporate their knowledge for making the final decisions of which customers to inspect. This work has resulted in appreciable results on a real-world data set of 3.6M customers. Our system is being deployed in a commercial NTL detection software. [less ▲]

Identifying material parameter distributions of fibers with extremely limited experimental efforts

Scientific Conference (2018, July 22)

Identifying niche mediated regulatory factors of stem cell phenotypic state: a systems biology approach

in FEBS Letters (2017)

IDENTIFYING PHOSPHO-PROTEOMIC PATTERNS INDICATIVE OF RESPONSIVENESS TO TARGETED THERAPY IN MELANOMA

Doctoral thesis (2018)

Identifying protein complexes directly from high-throughput TAP data with Markov random fields.

in BMC Bioinformatics (2007), 8

BACKGROUND: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the ... [more ▼]

BACKGROUND: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes. RESULTS: We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code under the GNU Public License can be found at http://algorithmics.molgen.mpg.de/Static/Supplements/ProteinComplexes. CONCLUSION: We can identify complexes in the data obtained from high-throughput experiments without prior elimination of proteins or weak interactions. The few parameters of our model, which does not rely on heuristics, can be estimated using maximum likelihood without a reference data set. This is particularly important for protein complex studies in organisms that do not have an established reference frame of known protein complexes. [less ▲]