Ultra lightweight PMMA-based composite plates with robust super-hydrophobic surfaces

Extremely lightweight plates made of an engineered PMMA-based composite material loaded with hollow glass micro-sized spheres, nano-sized silica particles and aluminum hydroxide prismatic micro-flakes were realized by cast molding. Their interesting bulk mechanical properties were combined to properly tailored surface topography compatible with the achievement of a superhydrophobic behavior after the deposition of a specifically designed hydrophobic coating. With this aim, we synthesized two different species of fluoromethacrylic polymers functionalized with methoxysilane anchoring groups to be covalently grafted onto the surface protruding inorganic fillers. By modulating the feed composition of the reacting monomers, it was possible to combine the hydrophobic character of the polymer with an high adhesion strength to the substrate and hence to maximize both the water contact angle (up to 157 degrees) and the durability of the easy-to-clean effect (up to 2000 h long outdoor exposure). (C) 2011 Elsevier Inc. All rights reserved

Continuous glucose monitoring identifies relationship between optimized glycemic control and post-discharge acute care facility needs

Abstract Objective Hyperglycemia is an independent risk factor in hospitalized patients for adverse outcomes, even if patients are not diabetic. We used continuous glucose monitoring to evaluate whether glycemic control (hyperglycemia) in the first 72 h after an intensive care admission was associated with the need for admission to a post discharge long-term medical facility. Results We enrolled 59 coronary artery bypass grafting patients. Poor glycemic control was defined as greater than 33% of continuous glucose monitoring values  180 mg/dL (group 1); and then these patients were reevaluated with a less strict definition of poor glycemic control with greater than 25% of continuous glucose values  180 mg/dL (group 2). In group 1 4/10 (40.0%) whose glucose was not well controlled went to an extended care post discharge facility as opposed to 6/49 (12.2%) that were well controlled. In reevaluation as group 2, 5/14 (35.7%) whose glucose was not well controlled went to an extended care post discharge facility as opposed to 5/45 (11.1%) who were well controlled. Admission to a post discharge facility was increased in patients with poor glycemic control p = 0.045 and p = 0.042 for group 1 and group 2, and with odds ratios of 4.8 (95% CI 1.0–22.5) and 4.4 (95% CI 1.0–19.4), respectively

Predicting Glycemia in Type 1 Diabetes Mellitus with Subspace-Based Linear Multistep Predictors

A major challenge for a person with diabetes is to adapt insulin dosage regimens and food intake to keep blood glucose within tolerable limits during daily life activities. The accurate prediction of blood glucose levels in response to inputs would support the patients with invaluable information for appropriate on-the-spot decision making concerning the management of the disease. Against this background, in this paper we propose multistep data-driven predictors to the purpose of predicting blood glucose multiple steps ahead in the future. We formulate the predictors based on the state-space construction step in subspace identification methods for linear systems. The clinical data of 14 type 1 diabetic patients collected during a 3-days long hospital visit were used. We exploited physiological models from the literature to filter the raw information on carbohydrate and insulin intakes in order to retrieve the inputs signals to the predictors. Predictions were based on the collected CGMS measurements, recalibrated against finger stick samples and smoothed through a regularization step. Performances were assessed with respect to YSI blood glucose samples and compared to those achieved with a Kalman filter identified from data. Results proved the competitiveness of the proposed approach

The LIBI Grid Platform for Bioinformatics

The LIBI project (International Laboratory of BioInformatics), which started in 2005 and will end in 2009, was initiated with the aim of setting up an advanced bioinformatics and computational biology laboratory, focusing on basic and applied research in modern biology and biotechnologies. One of the two goals of this project has been the development of a Grid Problem Solving Environment, built on top of EGEE, DEISA and SPACI infrastructures, to allow the submission and monitoring of jobs mapped to complex experiments in bioinformatics. In this work we describe the architecture of this environment and describe several case studies and related results which have been obtained using it

Multiple-Input Subject-Specific Modeling of Plasma Glucose Concentration for Feedforward Control

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an effective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC

The LIBI Grid Platform for Bioinformatics

The LIBI project (International Laboratory of BioInformatics), which started in 2005 and will end in 2009, was initiated with the aim of setting up an advanced bioinformatics and computational biology laboratory, focusing on basic and applied research in modern biology and biotechnologies. One of the 2 goals of this project has been the development of a Grid Problem Solving Environment, built on top of EGEE, DEISA and SPACI infrastructures, to allow the submission and monitoring of jobs mapped to complex experiments in bioinformatics. In this work we describe the architecture of this environment and describe several case studies and related results which have been obtained using it