BACKGROUND: Little evidence is available regarding the effects of long-term periodontal infection on diabetes mellitus (DM) control. The aim of this retrospective cohort study is to evaluate influence of periodontal status on changes of glycated hemoglobin (HbA1c) levels of patients with type 2 DM (DMt2). METHODS: Eighty patients (mean age: 56.0 +/- 8.9 years) with DMt2 were included. Patients were non-smokers, aged >/=40 years, and using antidiabetic drugs. Demographics, health history, and HbA1c levels were retrieved from medical charts. Probing depth and clinical attachment loss (AL) were recorded. RESULTS: Patients were examined at two time points within a mean interval of 38.6 +/- 6.6 months. Increase in HbA1c over time was statistically significant when severe periodontitis was diagnosed at baseline (2.32%, 95% confidence interval [CI]: 1.50% to 3.15%), in patients showing at least one tooth with >/=2 mm of AL progression (2.24%, 95% CI: 1.56% to 2.91%), in males (2.75%, 95% CI: 1.72% to 3.78%), and in those with HbA1c <6.5% at baseline (3.08%, 95% CI: 2.47% to 3.69%). After adjusting for baseline HbA1c, significant changes were still observed for severe periodontitis and progression of AL with increases of 0.85% and 0.9%, respectively. After adjusting for sex and HbA1c, AL progression was also statistically significant, with increases of 0.84%. CONCLUSIONS: Periodontitis progression was associated with increase in HbA1c in patients with DMt2. Identification of these risk factors suggests that periodontal treatment may improve glycemic control of patients with DMt2 by eliminating periodontal infection.

The aim of this paper is to disclose the new research developments and the results from the systematization of experience and user interaction with the Inspaedia (a new web knowledge " Agora "), to inspire a dynamic, collaborative, and interactive intelligence among the inspaediers. We will explain in detail and describe the design process and discuss the ultimate design interaction concept and development regarding (almost everything about) simplicity and playfulness of the inspaediers' experience to transform relevant information (related > mean-full > useful) in productive knowledge (inspiration > insight > foresight) in a very easy and quick way (usability: learnability; understandability; operability; attrac-tiveness…), with a smile in the face (satisfaction) and a wow in the mind (or in the soul).

Iterative Feedback Tuning (IFT) is a data-driven method used to tune parameters of feedback controllers minimising an H2 criterion. The method uses data from experiments to estimate the gradient of the criterion, and uses iterative quasinewton algorithms to adjust the controllers. When the method is used in cascade systems, usually the inner loop is firstly adjusted, and after the outer loop. In this article we describe an extension to the IFT method that adjusts both inner and outer loop at the same time using only data from closed-loop experiments at each iteration.

We provide √n-consistency results regarding estimation of the spectral representation of covariance operators of Hilbertian time series, in a setting with imperfect measurements. This is a generalization of the method developed in Bathia et al. (2010). The generalization relies on an important property of centered random elements in a separable Hilbert space, namely, that they lie almost surely in the closed linear span of the associated covariance operator. We provide a straightforward proof to this fact. This result is, to our knowledge, overlooked in the literature. It incidentally gives a rigorous formulation of Principal Component Analysis in Hilbert spaces.