University of Siena

About: University of Siena is a education organization based out in Siena, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 12179 authors who have published 33334 publications receiving 1008287 citations. The organization is also known as: Università degli studi di Siena & Universita degli studi di Siena.

Model Predictive Control Design: New Trends and Tools

Abstract: Model-based design is well recognized in industry as a systematic approach to the development, evaluation, and implementation of feedback controllers. Model predictive control (MPC) is a particular branch of model-based design: a dynamical model of the open-loop process is explicitly used to construct an optimization problem aimed at achieving the prescribed system's performance under specified restrictions on input and output variables. The solution of the optimization problem provides the feedback control action, and can be either computed by embedding a numerical solver in the real-time control code, or pre-computed off-line and evaluated through a lookup table of linear feedback gains. This paper reviews the basic ideas of MPC design, from the traditional linear MPC setup based on quadratic programming to more a advanced explicit and hybrid MPC, and highlights available software tools for the design, evaluation, code generation, and deployment of MPC controllers in real-time hardware platforms

Privacy-Preserving ECG Classification With Branching Programs and Neural Networks

Abstract: Privacy protection is a crucial problem in many biomedical signal processing applications. For this reason, particular attention has been given to the use of secure multiparty computation techniques for processing biomedical signals, whereby nontrusted parties are able to manipulate the signals although they are encrypted. This paper focuses on the development of a privacy preserving automatic diagnosis system whereby a remote server classifies a biomedical signal provided by the client without getting any information about the signal itself and the final result of the classification. Specifically, we present and compare two methods for the secure classification of electrocardiogram (ECG) signals: the former based on linear branching programs (a particular kind of decision tree) and the latter relying on neural networks. The paper deals with all the requirements and difficulties related to working with data that must stay encrypted during all the computation steps, including the necessity of working with fixed point arithmetic with no truncation while guaranteeing the same performance of a floating point implementation in the plain domain. A highly efficient version of the underlying cryptographic primitives is used, ensuring a good efficiency of the two proposed methods, from both a communication and computational complexity perspectives. The proposed systems prove that carrying out complex tasks like ECG classification in the encrypted domain efficiently is indeed possible in the semihonest model, paving the way to interesting future applications wherein privacy of signal owners is protected by applying high security standards.

Growth factors and myometrium: biological effects in uterine fibroid and possible clinical implications

Abstract: Background Growth factors are proteins secreted by a number of cell types that are capable of modulating cellular growth, proliferation and cellular differentiation. It is well accepted that uterine cellular events such as proliferation and differentiation are regulated by sex steroids and their actions in target tissues are mediated by local production of growth factors acting through paracrine and/or autocrine mechanisms. Myometrial mass is ultimately modified in pregnancy as well as in tumour conditions such as leiomyoma and leiomyosarcoma. Leiomyomas, also known as fibroids, are benign tumours of the uterus, considered to be one of the most frequent causes of infertility in reproductive years in women. Methods For this review, we searched the database MEDLINE and Google Scholar for articles with content related to growth factors acting on myometrium; the findings are hereby reviewed and discussed. Results Different growth factors such as epidermal growth factor (EGF), transforming growth factor-α (TGF-α), heparin-binding EGF (HB-EGF), acidic fibroblast growth factor (aFGF), basic fibroblast growth factor (bFGF), vascular endothelial growth factor (VEGF), insulin-like growth factor (IGF), platelet-derived growth factor (PDGF) and TGF-β perform actions in myometrium and in leiomyomas. In addition to these growth factors, activin and myostatin have been recently identified in myometrium and leiomyoma. Conclusions Growth factors play an important role in the mechanisms involved in myometrial patho-physiology.

Discovery of Very High Energy Gamma Radiation from IC 443 with the MAGIC Telescope

Abstract: We report the detection of a new source of very high energy (VHE; E-gamma >= 100 GeV) gamma- ray emission located close to the Galactic plane, MAGIC J0616 + 225, which is spatially coincident with supernova remnant IC 443. The observations were carried out with the MAGIC telescope in the periods 2005 December-2006 January and 2006 December-2007 January. Here we present results from this source, leading to a VHE gamma-ray signal with a statistical significance of 5.7 sigma in the 2006/2007 data and a measured differential gamma-ray flux consistent with a power law, described as dN(gamma)/(dA dt dE) = (1.0 +/- 0.2) x 10(-11) (E/0.4 TeV)(-3.1 +/- 0.3) cm(-2) s(-1) TeV-1. We briefly discuss the observational technique used and the procedure implemented for the data analysis. The results are placed in the context of the multiwavelength emission and the molecular environment found in the region of IC 443.