Parametric Continuous-Time Blind System Identification

In this paper, the blind system identification problem for continuous-time systems is considered. A direct continuous-time estimator is proposed by utilising a state-variable-filter least squares approach. In the proposed method, coupled terms between the numerator polynomial of the system and input parameters appear in the parameter vector which are subsequently separated using a rank-1 approximation. An algorithm is then provided for the direct identification of a single-input single-output linear time-invariant continuous-time system which is shown to satisfy the property of correctness under some mild conditions. Monte Carlo simulations demonstrate the performance of the algorithm and verify that a model and input signal can be estimated to a proportion of their true values

Estimating models with high-order noise dynamics using semi-parametric weighted null-space fitting

Standard system identification methods often provide inconsistent estimates with closed-loop data. With the prediction error method (PEM), this issue is solved by using a noise model that is flexible enough to capture the noise spectrum. However, a too flexible noise model (i.e., too many parameters) increases the model complexity, which can cause additional numerical problems for PEM. In this paper, we consider the weighted null-space fitting (WNSF) method. With this method, the system is first modeled using a non-parametric ARX model, which is then reduced to a parametric model of interest using weighted least squares. In the reduction step, a parametric noise model does not need to be estimated if it is not of interest. Because the flexibility of the noise model is increased with the sample size, this will still provide consistent estimates in closed loop and asymptotically efficient estimates in open loop. In this paper, we prove these results, and we derive the asymptotic covariance for the estimation error obtained in closed loop, which is optimal for an infinite-order noise model. For this purpose, we also derive a new technical result for geometric variance analysis, instrumental to our end. Finally, we perform a simulation study to illustrate the benefits of the method when the noise model cannot be parametrized by a low-order model.QC 20190326</p

Amaurosis bilateral cortical en preeclampsia severa

Se presenta un caso clínico con diagnóstico final de preeclampsia severa que debuta con una complicación excepcional, la amaurosis bilateral cortical. Se discute esta presentación poco frecuente, realizándose una revisión actualizadaWe report a clinical case of severe preeclampsia, which had a severe rare complication, cortical blindness. We discuss this uncommon complication, and an actualized review is make

Joint effect among p53, CYP1A1, GSTM1 polymorphism combinations and smoking on prostate cancer risk: an exploratory genotype-environment interaction study

Aim: To assess the role of several genetic factors in combination with an environmental factor as modulators of prostate cancer risk. We focus on allele variants of low-penetrance genes associated with cell control, the detoxification processes and smoking. Methods: In a case-control study we compared people carrying p53cd72 Pro allele, CYP1A1 M1 allele and GSTM1 null genotypes with their prostate cancer risk. Results: The joint risk for smokers carrying Pro* and M1*, Pro* and GSTM1null or GSTM1 null and CYP1A1 M1* variants was significantly higher (odds ratio [OR]: 13.13, 95% confidence interval [CI]: 2.41-71.36; OR: 3.97, 95% CI: 1.13-13.95 and OR: 6.87, 95% Cl: 1.68-27.97, respectively) compared with that for the reference group, and for non-smokers was not significant. OR for combinations among p53cd72, GSTM1 and CYP1A1 M I in smokers were positively and significantly associated with prostate cancer risk compared with non-smokers and compared with the Putative lowest risk group (OR: 8.87, 95% CI: 1.25-62.71). Conclusion: Our results suggest that a combination of p53cd72, CYP1A1, GSTM1 alleles and smoking plays a significant role in modified prostate cancer risk on the study population, which means that smokers carrying susceptible genotypes might have a significantly higher risk than those carrying non-susceptible genotypes