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See detailA novel heterozygous OPA3 mutation located in the mitochondrial target sequence results in altered steady-state levels and fragmented mitochondrial network.
Grau, Tanja; Burbulla, Lena F.; Engl, Gertraud et al

in Journal of medical genetics (2013), 50(12), 848-58

BACKGROUND: Mutations in OPA3 have been reported in patients with autosomal dominant optic atrophy plus cataract and Costeff syndrome. Here, we report the results of a comprehensive study on OPA3 ... [more ▼]

BACKGROUND: Mutations in OPA3 have been reported in patients with autosomal dominant optic atrophy plus cataract and Costeff syndrome. Here, we report the results of a comprehensive study on OPA3 mutations, including the mutation spectrum and its prevalence in a large cohort of OPA1-negative autosomal dominant optic atrophy (ADOA) patients, the associated clinical phenotype and the functional characterisation of a newly identified OPA3 mutant. METHODS: Mutation analysis was carried out in a patient cohort of 121 independent ADOA patients. To characterise a novel OPA3 mutation, we analysed the mitochondrial import, steady-state levels and the mitochondrial localisation of the mutated protein in patients' fibroblasts. Furthermore, the morphology of mitochondria harbouring the mutated OPA3 was monitored. RESULTS: We identified four independent cases (representing families with multiple affected members) with OPA3 mutations. Besides the known p.Q105E mutation, we observed a novel insertion, c.10_11insCGCCCG/p.V3_G4insAP which is located in the mitochondrial presequence. Detailed functional analysis of mitochondria harbouring this novel mutation demonstrates a fragmented mitochondrial network with a decreased mitochondrial mass in patient fibroblasts. In addition, quantification of the OPA3 protein reveals decreased steady-state levels of the mutant protein compared with the native one. Comparison of the clinical phenotypes suggests that OPA3 mutations can additionally evoke hearing loss and by that extend the clinical manifestation of OPA3-associated optic atrophy. This finding is supported by expression analysis of OPA3 in murine cochlear tissue. CONCLUSIONS: In summary, our study provides new insights into the clinical spectrum and the pathogenesis of dominant optic atrophy caused by mutations in the OPA3 gene. [less ▲]

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See detailNovel homozygous p.E64D mutation in DJ1 in early onset Parkinson disease (PARK7).
Hering, Robert; Strauss, Karsten M.; Tao, Xiao et al

in Human mutation (2004), 24(4), 321-9

Mutations in the parkin gene have been identified as a common cause of autosomal recessive inherited Parkinson disease (PD) associated with early disease manifestation. However, based on linkage data ... [more ▼]

Mutations in the parkin gene have been identified as a common cause of autosomal recessive inherited Parkinson disease (PD) associated with early disease manifestation. However, based on linkage data, mutations in other genes contribute to the genetic heterogeneity of early-onset PD (EOPD). Recently, two mutations in the DJ1 gene were described as a second cause of autosomal recessive EOPD (PARK7). Analyzing the PARK7/DJ1 gene in 104 EOPD patients, we identified a third mutation, c.192G>C (p.E64D), associated with EOPD in a patient of Turkish ancestry and characterized the functional significance of this amino acid substitution. In the patient, a substantial reduction of dopamine uptake transporter (DAT) binding was found in the striatum using [(18)F]FP-CIT and PET, indicating a serious loss of presynaptic dopaminergic afferents. His sister, homozygous for E64D, was clinically unaffected but showed reduced dopamine uptake when compared with a clinically unaffected brother, who is heterozygous for E64D. We demonstrate by crystallography that the E64D mutation does not alter the structure of the DJ1 protein, however we observe a tendency towards decreased levels of the mutant protein when overexpressed in HEK293 or COS7 cells. Using immunocytochemistry in contrast to the homogenous nuclear and cytoplasmic staining in HEK293 cells overexpressing wild-type DJ1, about 5% of the cells expressing E64D and up to 80% of the cells expressing the recently described L166P mutation displayed a predominant nuclear localization of the mutant DJ1 protein. [less ▲]

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See detailNovel human hepatic organoid model enables testing of drug-induced liver fibrosis in vitro.
Leite, Sofia B.; Roosens, Tiffany; El Taghdouini, Adil et al

in Biomaterials (2016), 78

Current models for in vitro fibrosis consist of simple mono-layer cultures of rodent hepatic stellate cells (HSC), ignoring the role of hepatocyte injury. We aimed to develop a method allowing the ... [more ▼]

Current models for in vitro fibrosis consist of simple mono-layer cultures of rodent hepatic stellate cells (HSC), ignoring the role of hepatocyte injury. We aimed to develop a method allowing the detection of hepatocyte-mediated and drug-induced liver fibrosis. We used HepaRG (Hep) and primary human HSCs cultured as 3D spheroids in 96-well plates. These resulting scaffold-free organoids were characterized for CYP induction, albumin secretion, and hepatocyte and HSC-specific gene expression by qPCR. The metabolic competence of the organoid over 21 days allows activation of HSCs in the organoid in a drug- and hepatocyte-dependent manner. After a single dose or repeated exposure for 14 days to the pro-fibrotic compounds Allyl alcohol and Methotrexate, hepatic organoids display fibrotic features such as HSC activation, collagen secretion and deposition. Acetaminophen was identified by these organoids as an inducer of hepatotoxic-mediated HSC activation which was confirmed in vivo in mice. This novel hepatic organoid culture model is the first that can detect hepatocyte-dependent and compound-induced HSC activation, thereby representing an important step forward towards in vitro compound testing for drug-induced liver fibrosis. [less ▲]

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See detailA novel immunofluorescent assay to investigate oxidative phosphorylation deficiency in mitochondrial myopathy: understanding mechanisms and improving diagnosis.
Rocha, Mariana C.; Grady, John P.; Grünewald, Anne UL et al

in Scientific reports (2015), 5

Oxidative phosphorylation defects in human tissues are often challenging to quantify due to a mosaic pattern of deficiency. Biochemical assays are difficult to interpret due to the varying enzyme ... [more ▼]

Oxidative phosphorylation defects in human tissues are often challenging to quantify due to a mosaic pattern of deficiency. Biochemical assays are difficult to interpret due to the varying enzyme deficiency levels found in individual cells. Histochemical analysis allows semi-quantitative assessment of complex II and complex IV activities, but there is no validated histochemical assay to assess complex I activity which is frequently affected in mitochondrial pathology. To help improve the diagnosis of mitochondrial disease and to study the mechanisms underlying mitochondrial abnormalities in disease, we have developed a quadruple immunofluorescent technique enabling the quantification of key respiratory chain subunits of complexes I and IV, together with an indicator of mitochondrial mass and a cell membrane marker. This assay gives precise and objective quantification of protein abundance in large numbers of individual muscle fibres. By assessing muscle biopsies from subjects with a range of different mitochondrial genetic defects we have demonstrated that specific genotypes exhibit distinct biochemical signatures in muscle, providing evidence for the diagnostic use of the technique, as well as insight into the underlying molecular pathology. Stringent testing for reproducibility and sensitivity confirms the potential value of the technique for mechanistic studies of disease and in the evaluation of therapeutic approaches. [less ▲]

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See detailA novel immunomodulator, FTY-720 reverses existing cardiac hypertrophy and fibrosis from pressure overload by targeting NFAT (nuclear factor of activated T-cells) signaling and periostin.
Liu, Wei; Zi, Min; Tsui, Hoyee et al

in Circulation. Heart failure (2013), 6(4), 833-44

BACKGROUND: Hypertension or aortic stenosis causes pressure overload, which evokes hypertrophic myocardial growth. Sustained cardiac hypertrophy eventually progresses to heart failure. Growing evidence ... [more ▼]

BACKGROUND: Hypertension or aortic stenosis causes pressure overload, which evokes hypertrophic myocardial growth. Sustained cardiac hypertrophy eventually progresses to heart failure. Growing evidence indicates that restraining hypertrophy could be beneficial; here, we discovered that FTY-720, an immunomodulator for treating multiple sclerosis, can reverse existing cardiac hypertrophy/fibrosis. METHODS AND RESULTS: Male C57/Bl6 mice underwent transverse aortic constriction (TAC) for 1 week followed by FTY-720 treatment for 2 weeks under continuing TAC. Compared with vehicle-treated TAC hearts, FTY-720 significantly reduced ventricular mass, ameliorated fibrosis, and improved cardiac performance. Mechanistic studies led us to discover that FTY-720 appreciably inhibited nuclear factor of activated T-cells (NFAT) activity. Moreover, we found that in primary cardiomyocytes (rat and human) pertussis toxin (Gi-coupled receptor inhibitor) substantially blocked the antihypertrophic effect of FTY-720. This observation was confirmed in a mouse model of pressure overload. Interestingly, gene array analysis of TAC hearts revealed that FTY-720 profoundly decreased gene expression of a group of matricellular proteins, of which periostin was prominent. Analysis of periostin protein expression in TAC-myocardium, as well as in rat and human cardiac fibroblasts, confirmed the array data. Moreover, we found that FTY-720 treatment or knockdown of periostin protein was able to inhibit transforming growth factor-beta responsiveness and decrease collagen expression. CONCLUSIONS: FTY-720 alleviates existing cardiac hypertrophy/fibrosis through mechanisms involving negative regulation of NFAT activity in cardiomyocytes and reduction of periostin expression allowing for a more homeostatic extracellular compartment milieu. Together, FTY-720 or its analogues could be a promising new approach for treating hypertrophic/fibrotic heart disease. [less ▲]

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See detailA Novel InAlAs/InGaAs Layer Structure for Monolithically Integrated Photoreceiver,
Hodel, U.; Orzati, A.; Marso, Michel UL et al

in Proc. 2000 Int. Conf. Indium Phosphide and Related Materials (2000)

Detailed reference viewed: 69 (0 UL)
See detailA Novel InGaAs Schottky-2DEG Diode
Marso, Michel UL; Kordoš, P.; Fox, A. et al

in Proceedings of the 5th International Conference on InP and Related Compounds, Paris, France (1993)

Detailed reference viewed: 108 (0 UL)
See detailNovel InP/GaInAs Photodetector for Integration in HEMT Circuits
Horstmann, M.; Marso, Michel UL; Schimpf, K. et al

in Proceedings of the 25th European Solid State Devices Research Conference, Den Haag, The Netherlands (1995)

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See detailA Novel InP/InGaAs Photodetector Based on a 2DEG layer structure
Marso, Michel UL; Horstmann, M.; Rüders, F. et al

in Proceedings of the 6th International Conference on InP and Related Compounds, Santa Barbara, California USA (1994)

Detailed reference viewed: 62 (0 UL)
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See detailNovel Insight into the Role of the S100A8/A9 Protein Complex in the Regulation of Neutrophil Functions
Jung, Nicolas UL

Doctoral thesis (2019)

S100A8 and S100A9 are members of the S100 family of cytoplasmic EF-hand calcium-binding proteins and are abundantly expressed in the cytosol of neutrophils. Mostly found under heterodimeric form, S100A8 ... [more ▼]

S100A8 and S100A9 are members of the S100 family of cytoplasmic EF-hand calcium-binding proteins and are abundantly expressed in the cytosol of neutrophils. Mostly found under heterodimeric form, S100A8/A9 have various intracellular and extracellular functions; they act as alarmins, amplifying the host inflammatory response. Our previous study showed that the intracellular activity of S100A8/A9 is carried by the phosphorylation of S100A9. Based on these results, we further investigated the importance of this post-translational modification on the extracellular activity of the protein complex and its impact on the inflammatory functions of neutrophils. First, we analyzed the phosphorylation state of secreted S100A8/A9 and the mechanism by which the protein complex is released into the extracellular space. Our results show that S100A9 is secreted under a phosphorylated form within the S100A8/A9 protein complex and this release is highly correlated to the process of NETosis. Next, we investigated the inflammatory response of neutrophil-like dHL-60 cells when stimulated with the phosphorylated and non-phosphorylated form of S100A8/A9. Our results indicate that only the phosphorylated form of S100A8/A9 increases the expression and secretion of various cytokines (e.g. TNFa, CCL4, CXCL8). Using receptor-neutralizing antibodies, we then determined the receptor and signaling pathways associated to S100A8/A9-P-induced cytokine secretion. The reduction of expression levels of the previously mentioned cytokines, after TLR4 blocking, point out that S100A8/A9-P-induced signaling is mediated in part by TLR4. Finally, we investigated the post-transcriptional response induced by S100A8/A9-P stimulation. Using miRNA-sequencing of S100A8/A9-P stimulated dHL-60 cells, we identified an upregulation of miR-146a-5p, miR-146b-5p and miR-155-5p expression. Since these three microRNAs have previously been described to regulate TLR4 signaling at various levels, we investigated their influence on the inflammatory response mediated by S100A8/A9-P. Stable overexpression of miR-146a-5p and miR-155-5p in dHL-60 cells resulted in the reduced S100A8/A9-P-mediated secretion of cytokines through the inhibition of key players in the TLR4 signaling pathways. To summarize, our results give new insight into the pro-inflammatory functions induced by S100A8/A9-P in neutrophils and reveal the potential of the phosphorylated protein complex as a major regulator of inflammation in chronic inflammatory diseases. [less ▲]

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See detailA novel iterative convex approximation method
Yang, Yang UL; Pesavento, Marius

in Proc. IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (2015, August)

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See detailA novel line search method for nonsmooth optimization problems
Yang, Yang UL; Pesavento, Marius

in Proc. 23rd European Signal Processing Conference (2015, August)

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See detailNovel Methods for Learning and Adaptation in Chemical Reaction Networks
Banda, Peter UL

Doctoral thesis (2015)

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be re-programmed or trained once fabricated. The implementation of adaptive ... [more ▼]

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be re-programmed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. Currently, biochemistry lacks a systems vision on how the user-level programming interface and abstraction with a subsequent translation to chemistry should look like. By developing adaptation in chemistry, we could replace multiple hard-wired systems with a single programmable template that can be (re)trained to match a desired input-output profile benefiting smart drug delivery, pattern recognition, and chemical computing. I aimed to address these challenges by proposing several approaches to learning and adaptation in Chemical Reaction Networks (CRNs), a type of simulated chemistry, where species are unstructured, i.e., they are identified by symbols rather than molecular structure, and their dynamics or concentration evolution are driven by reactions and reaction rates that follow mass-action and Michaelis-Menten kinetics. Several CRN and experimental DNA-based models of neural networks exist. However, these models successfully implement only the forward-pass, i.e., the input-weight integration part of a perceptron model. Learning is delegated to a non-chemical system that computes the weights before converting them to molecular concentrations. Autonomous learning, i.e., learning implemented fully inside chemistry has been absent from both theoretical and experimental research. The research in this thesis offers the first constructive evidence that learning in CRNs is, in fact, possible. I have introduced the original concept of a chemical binary perceptron that can learn all 14 linearly-separable logic functions and is robust to the perturbation of rate constants. That shows learning is universal and substrate-free. To simplify the model I later proposed and applied the ``asymmetric" chemical arithmetic providing a compact solution for representing negative numbers in chemistry. To tackle more difficult tasks and to serve more complicated biochemical applications, I introduced several key modular building blocks, each addressing certain aspects of chemical information processing and learning. These parts organically combined into gradually more complex systems. First, instead of simple static Boolean functions, I tackled analog time-series learning and signal processing by modeling an analog chemical perceptron. To store past input concentrations as a sliding window I implemented a chemical delay line, which feeds the values to the underlying chemical perceptron. That allows the system to learn, e.g., the linear moving-average and to some degree predict a highly nonlinear NARMA benchmark series. Another important contribution to the area of chemical learning, which I have helped to shape, is the composability of perceptrons into larger multi-compartment networks. Each compartment hosts a single chemical perceptron and compartments communicate with each other through a channel-mediated exchange of molecular species. Besides the feedforward pass, I implemented the chemical error backpropagation analogous to that of feedforward neural networks. Also, after applying mass-action kinetics for the catalytic reactions, I succeeded to systematically analyze the ODEs of my models and derive the closed exact and approximative formulas for both the input-weight integration and the weight update with a learning rate annealing. I proved mathematically that the formulas of certain chemical perceptrons equal the formal linear and sigmoid neurons, essentially bridging neural networks and adaptive CRNs. For all my models the basic methodology was to first design species and reactions, and then set the rate constants either "empirically" by hand, automatically by a standard genetic algorithm (GA), or analytically if possible. I performed all simulations in my COEL framework, which is the first cloud-based chemistry modeling tool, accessible at http://coel-sim.org. I minimized the amount of required molecular species and reactions to make wet chemical implementation possible. I applied an automatized mapping technique, Soloveichik's CRN-to-DNA-strand-displacement transformation, to the chemical linear perceptron and the manual signalling delay line and obtained their full DNA-strand specified implementations. As an alternative DNA-based substrate, I mapped these two models also to deoxyribozyme-mediated cleavage reactions reducing the size of the displacement variant to a third. Both DNA-based incarnations could directly serve as blue-prints for wet biochemicals. Besides an actual synthesis of my models and conducting an experiment in a biochemical laboratory, the most promising future work is to employ so-called reservoir computing (RC), which is a novel machine learning method based on recurrent neural networks. The RC approach is relevant because for time-series prediction it is clearly superior to classical recurrent networks. It can also be implemented in various ways, such as electrical circuits, physical systems, such as a colony of Escherichia Coli, and water. RC's loose structural assumptions therefore suggest that it could be expressed in a chemical form as well. This could further enhance the expressivity and capabilities of chemically-embedded learning. My chemical learning systems may have applications in the area of medical diagnosis and smart medication, e.g., concentration signal processing and monitoring, and the detection of harmful species, such as chemicals produced by cancer cells in a host (cancer miRNAs) or the detection of a severe event, defined as a linear or nonlinear temporal concentration pattern. My approach could replace “hard-coded” solutions and would allow to specify, train, and reuse chemical systems without redesigning them. With time-series integration, biochemical computers could keep a record of changing biological systems and act as diagnostic aids and tools in preventative and highly personalized medicine. [less ▲]

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See detailNovel Methods for Multi-Shape Analysis
Bernard, Florian UL

Doctoral thesis (2016)

Multi-shape analysis has the objective to recognise, classify, or quantify morphological patterns or regularities within a set of shapes of a particular object class in order to better understand the ... [more ▼]

Multi-shape analysis has the objective to recognise, classify, or quantify morphological patterns or regularities within a set of shapes of a particular object class in order to better understand the object class of interest. One important aspect of multi-shape analysis are Statistical Shape Models (SSMs), where a collection of shapes is analysed and modelled within a statistical framework. SSMs can be used as (statistical) prior that describes which shapes are more likely and which shapes are less likely to be plausible instances of the object class of interest. Assuming that the object class of interest is known, such a prior can for example be used in order to reconstruct a three-dimensional surface from only a few known surface points. One relevant application of this surface reconstruction is 3D image segmentation in medical imaging, where the anatomical structure of interest is known a-priori and the surface points are obtained (either automatically or manually) from images. Frequently, Point Distribution Models (PDMs) are used to represent the distribution of shapes, where each shape is discretised and represented as labelled point set. With that, a shape can be interpreted as an element of a vector space, the so-called shape space, and the shape distribution in shape space can be estimated from a collection of given shape samples. One crucial aspect for the creation of PDMs that is tackled in this thesis is how to establish (bijective) correspondences across the collection of training shapes. Evaluated on brain shapes, the proposed method results in an improved model quality compared to existing approaches whilst at the same time being superior with respect to runtime. The second aspect considered in this work is how to learn a low-dimensional subspace of the shape space that is close to the training shapes, where all factors spanning this subspace have local support. Compared to previous work, the proposed method models the local support regions implicitly, such that no initialisation of the size and location of these regions is necessary, which is advantageous in scenarios where this information is not available. The third topic covered in this thesis is how to use an SSM in order to reconstruct a surface from only few surface points. By using a Gaussian Mixture Model (GMM) with anisotropic covariance matrices, which are oriented according to the surface normals, a more surface-oriented fitting is achieved compared to a purely point-based fitting when using the common Iterative Closest Point (ICP) algorithm. In comparison to ICP we find that the GMM-based approach gives superior accuracy and robustness on sparse data. Furthermore, this work covers the transformation synchronisation method, which is a procedure for removing noise that accounts for transitive inconsistency in the set of pairwise linear transformations. One interesting application of this methodology that is relevant in the context of multi-shape analysis is to solve the multi-alignment problem in an unbiased/reference-free manner. Moreover, by introducing an improvement of the numerical stability, the methodology can be used to solve the (affine) multi-image registration problem from pairwise registrations. Compared to reference-based multi-image registration, the proposed approach leads to an improved registration accuracy and is unbiased/reference-free, which makes it ideal for statistical analyses. [less ▲]

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See detailA Novel Model-Predictive Cruise Controller for Electric Vehicles and Energy-Efficient Driving
Schwickart, Tim Klemens UL; Voos, Holger UL; Minaglou, Jean-Régis UL et al

in A Novel Model-Predictive Cruise Controller for Electric Vehicles and Energy-Efficient Driving (2014, July)

This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. A predictive eco-cruise controller involves the minimisation of a compromise between terms ... [more ▼]

This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. In this work, a coordinate transformation is used which leads to a linear differential motion equation without loss of information. The energy consumption is modeled by the maximum of a set of linear functions which is determined implicitly by the optimisation problem and thus leads to a piecewise linear model. The reformulations finally result in a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. The controller and the underlying dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified by measurements. Simulation results of the MPC controller and the simulation model in closed-loop operation finally provide a proof of concept. [less ▲]

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See detailNovel MSM-2DEG PD/HEMT Photoreceiver for 10 Gbit/s Operation
Horstmann, M.; Muttersbach, J.; v.d.Hart, A. et al

in Proceedings of the 26th European Solid State Devices Research Conference, Bologna, Italy (1996)

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See detailA Novel Multi-objectivisation Approach for Optimising the Protein Inverse Folding Problem
Nielsen, Sune Steinbjorn UL; Danoy, Grégoire UL; Jurkowski, Wiktor et al

in Applications of Evolutionary Computation: 18th European Conference, EvoApplications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings (2015)

In biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of the living cell, but also to design proteins of new functions. The Inverse Folding ... [more ▼]

In biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of the living cell, but also to design proteins of new functions. The Inverse Folding Problem (IFP) is in itself an important research problem, but also at the heart of most rational protein design approaches. In brief, the IFP consists in finding sequences that will fold into a given structure, rather than determining the structure for a given sequence - as in conventional structure prediction. In this work we present a Multi Objective Genetic Algorithm (MOGA) using the diversity-as-objective (DAO) variant of multi-objectivisation, to optimise secondary structure similarity and sequence diversity at the same time, hence pushing the search farther into wide-spread areas of the sequence solution-space. To control the high diversity generated by the DAO approach, we add a novel Quantile Constraint (QC) mechanism to discard an adjustable worst quantile of the population. This DAO-QC approach can efficiently emphasise exploitation rather than exploration to a selectable degree achieving a trade-off producing both better and more diverse sequences than the standard Genetic Algorithm (GA). To validate the final results, a subset of the best sequences was selected for tertiary structure prediction. The super-positioning with the original protein structure demonstrated that meaningful sequences are generated underlining the potential of this work. [less ▲]

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See detailA Novel Network Integrating a miRNA-203/SNAI1 Feedback Loop which Regulates Epithelial to Mesenchymal Transition.
Moes, Michèle UL; Le Béchec, Antony UL; Crespo, Isaac UL et al

in PLoS ONE (2012), 7(4), 35440

Background: The majority of human cancer deaths are caused by metastasis. The metastatic dissemination is initiated by the breakdown of epithelial cell homeostasis. During this phenomenon, referred to as ... [more ▼]

Background: The majority of human cancer deaths are caused by metastasis. The metastatic dissemination is initiated by the breakdown of epithelial cell homeostasis. During this phenomenon, referred to as epithelial to mesenchymal transition (EMT), cells change their genetic and trancriptomic program leading to phenotypic and functional alterations. The challenge of understanding this dynamic process resides in unraveling regulatory networks involving master transcription factors (e.g. SNAI1/2, ZEB1/2 and TWIST1) and microRNAs. Here we investigated microRNAs regulated by SNAI1 and their potential role in the regulatory networks underlying epithelial plasticity. Results: By a large-scale analysis on epithelial plasticity, we highlighted miR-203 and its molecular link with SNAI1 and the miR-200 family, key regulators of epithelial homeostasis. During SNAI1-induced EMT in MCF7 breast cancer cells, miR-203 and miR-200 family members were repressed in a timely correlated manner. Importantly, miR-203 repressed endogenous SNAI1, forming a double negative miR203/SNAI1 feedback loop. We integrated this novel miR203/SNAI1 with the known miR200/ZEB feedback loops to construct an a priori EMT core network. Dynamic simulations revealed stable epithelial and mesenchymal states, and underscored the crucial role of the miR203/SNAI1 feedback loop in state transitions underlying epithelial plasticity. Conclusion: By combining computational biology and experimental approaches, we propose a novel EMT core network integrating two fundamental negative feedback loops, miR203/SNAI1 and miR200/ZEB. Altogether our analysis implies that this novel EMT core network could function as a switch controlling epithelial cell plasticity during differentiation and cancer progression. [less ▲]

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See detailA novel numerical integration technique over arbitrary polygons
Natarajan, Sundararajan; Mahapatra, D Roy; Bordas, Stéphane UL et al

Scientific Conference (2009, April)

In this paper, a new numerical integration technique [1] on arbitrary polygons is presented. The polygonal do- main is mapped conformally to the unit disk using Schwarz-Christoffel mapping [2] and a ... [more ▼]

In this paper, a new numerical integration technique [1] on arbitrary polygons is presented. The polygonal do- main is mapped conformally to the unit disk using Schwarz-Christoffel mapping [2] and a midpoint quadrature rule defined on the unit circle is used. This method eliminates the need for a two level isoparametric mapping usuall required [3]. Moreover the positivity of the Jacobian is guaranteed. We present numerical results for a few benchmark problems in the context of polygonal finite elements that show the effectiveness of the method. [less ▲]

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See detailNovel Opportunities by Laser Welding of Dissimilar Materials
Schiry, Marc UL; Plapper, Peter UL

in Proceedings of International Conference on Competitive Manufacturing COMA'19 (2019, January)

The Laser Technology Competence Centre (LTCC) of the University of Luxembourg provides skills in joining material combinations, which are considered being non-weld able with traditional methods or at ... [more ▼]

The Laser Technology Competence Centre (LTCC) of the University of Luxembourg provides skills in joining material combinations, which are considered being non-weld able with traditional methods or at least challenging to join (e.g. Copper and Aluminium, Aluminium and Polyamide (PA), Titanium and PEEK, steel and tungsten hard alloy). Related accomplishments include minimal intermetallic compounds, convincing mechanical and superior electrical properties of the laser-welded specimen. With defined spatial and temporal modulation of the laser beam, an accurately defined temperature profile is created on the lower side of the upper material in the two dimensional directions, which enable joining of dissimilar materials in overlap configurations, despite challenging thermal properties. To weld butt-joint geometries with minimal heat affected volume, the temperature profiles were expanded in the third dimension, which can be achieved through controlled laser energy guidance. The scientific methods to accomplish these convincing results are explained, with selected industrial use cases ranging from automotive industry, energy storage, and medical implants. An outlook with unsolved challenges is intended to ignite discussions about upcoming research topics. [less ▲]

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