An IGF-I promoter polymorphism modifies the relationships between birth weight and risk factors for cardiovascular disease and diabetes at age 36

OBJECTIVE: To investigate whether IGF-I promoter polymorphism was associated with birth weight and risk factors for cardiovascular disease (CVD) and type 2 diabetes (T2DM), and whether the birth weight – risk factor relationship was the same for each genotype. DESIGN AND PARTICIPANTS: 264 subjects (mean age 36 years) had data available on birth weight, IGF-I promoter polymorphism genotype, CVD and T2DM risk factors. Student's t-test and regression analyses were applied to analyse differences in birth weight and differences in the birth weight – risk factors relationship between the genotypes. RESULTS: Male variant carriers (VCs) of the IGF-I promoter polymorphism had a 0.2 kg lower birth weight than men with the wild type allele (p = 0.009). Of the risk factors for CVD and T2DM, solely LDL concentration was associated with the genotype for the polymorphism. Most birth weight – risk factor relationships were stronger in the VC subjects; among others the birth weight – systolic blood pressure relationship: 1 kg lower birth weight was related to an 8.0 mmHg higher systolic blood pressure CONCLUSION: The polymorphism in the promoter region of the IGF-I gene is related to birth weight in men only, and to LDL concentration only. Furthermore, the genotype for this polymorphism modified the relationships between birth weight and the risk factors, especially for systolic and diastolic blood pressure

Modeling of miRNA and Drug Action in the EGFR Signaling Pathway

MicroRNAs have gained significant interest due to their widespread occurrence and diverse functions as regulatory molecules, which are essential for cell division, growth, development and apoptosis in eukaryotes. The epidermal growth factor receptor (EGFR) signaling pathway is one of the best investigated cellular signaling pathways regulating important cellular processes and its deregulation is associated with severe diseases, such as cancer. In this study, we introduce a systems biological model of the EGFR signaling pathway integrating validated miRNA-target information according to diverse studies, in order to demonstrate essential roles of miRNA within this pathway. The model consists of 1241 reactions and contains 241 miRNAs. We analyze the impact of 100 specific miRNA inhibitors (anit-miRNAs) on this pathway and propose that the embedded miRNA-network can help to identify new drug targets of the EGFR signaling pathway and thereby support the development of new therapeutic strategies against cancer

A 3′-Untranslated Region (3′UTR) Induces Organ Adhesion by Regulating miR-199a* Functions

Mature microRNAs (miRNAs) are single-stranded RNAs of 18–24 nucleotides that repress post-transcriptional gene expression. However, it is unknown whether the functions of mature miRNAs can be regulated. Here we report that expression of versican 3′UTR induces organ adhesion in transgenic mice by modulating miR-199a* activities. The study was initiated by the hypothesis that the non-coding 3′UTR plays a role in the regulation of miRNA function. Transgenic mice expressing a construct harboring the 3′UTR of versican exhibits the adhesion of organs. Computational analysis indicated that a large number of microRNAs could bind to this fragment potentially including miR-199a*. Expression of versican and fibronectin, two targets of miR-199a*, are up-regulated in transgenic mice, suggesting that the 3′UTR binds and modulates miR-199a* activities, freeing mRNAs of versican and fibronectin from being repressed by miR-199a*. Confirmation of the binding was performed by PCR using mature miR-199a* as a primer and the targeting was performed by luciferase assays. Enhanced adhesion by expression of the 3′UTR was confirmed by in vitro assays. Our results demonstrated that upon arrival in cytoplasm, miRNA activities can be modulated locally by the 3′UTR. Our assay may be developed as sophisticated approaches for studying the mutual regulation of miRNAs and mRNAs in vitro and in vivo. We anticipate that expression of the 3′UTR may be an approach in the development of gene therapy

Identification for control of high-tech motion systems

abstract onl

Numerically reliable identification of fast sampled systems:a novel δ-domain data-dependent orthonormal polynomial approach

\u3cp\u3eThe practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in the δ -domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example.\u3c/p\u3

Numerically reliable identification of fast sampled systems:a novel delta-domain data-dependent orthonormal polynomial approach

Abstract— The practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in\u3cbr/\u3ethe δ-domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example

Inferential control of a wafer stage using disturbance observers

In high-precision motion systems it is often not possible to directly measure at the location where performance is required. Therefore, performance variables need to be inferred from non-collocated sensor measurements. If flexible behavior is negligible, a static sensor transformation is used to find a rigid-body (RB) approximation of the performance variable. However, for next-generation motion systems positioning accuracy is ever-increasing, leading to a situation where flexible dynamics are not negligible. As a result, traditional single degree-of-freedom (DOF) controllers are inadequate [1]. The aim of this research is to control the unmeasured performance variable while taking flexible behavior into account, through 2-DOF controller structures and disturbance observers

Parametrizing mechanical systems using matrix fraction descriptions:with application to spatio-temporal identification

Identifying accurate Linear Parameter Varying models of flexible mechanical systems is crucial for the inference and control of unmeasured performance variables in high-performance mechatronic systems. A succesfull grey-box strategy to obtain accurate models through a local approach is to directly identify canonical mechanical systems, whose parameters can subsequently be interpolated in a physically motivated manner. Estimating these generally nonlinear-in-the parameter modal models from Frequency Response Function data through gradient-based techniques suffers from the potential of converging to sub-optimal estimates. In this research proposes a novel Matrix-Fraction Description parametrisation that is shown to be equivalent to the relevant class of modal mechanical systems. This parametrisation enables the formulation of the Sanathanan-and-Koerner and Instrumental Variables estimators which are known to attain the global optimum upon convergence.\u3cbr/\u3eThe proposed parametrisation is successfully employed to directly estimate a large set of local models of various position-dependent motion systems, thereby confirming that proposed the identification method is well-suited for practical applications

Identification for motion control:incorporating constraints and numerical considerations

Frequency domain identification is a common starting point for model based motion control. The aim of this paper is to tailor parametric identification methods to the specific class of motion systems. The proposed method involves two aspects: 1) incorporating prior knowledge, e.g. it is often known beforehand that the system exhibits rigid-body behavior, and 2) numerical reliability, since the considered class of systems is challenging to identify. The result is a frequency domain algorithm that is particularly suited for the identification of lightly damped motion systems with rigid body behavior, a high model order, and large input- output dimensions. Experimental results on a prototype next-generation motion system clearly demonstrate the advantages of the proposed approach