Microvascular alterations in hypertension and vascular aging

Hypertension and aging are characterized by vascular remodelling and stiffness as well as endothelial dysfunction. Endothelial function declines with age, since aging is associated with senescence of the endothelium due to increased rate of apoptosis and reduced regenerative capacity of the endothelium. Different phenotypes of hypertension have been described in younger and adult subjects with hypertension. In younger patients functional and structural alterations of resistance arteries occur as the earliest vascular alterations which have prognostic significance and may contribute to stiffness of large arteries through wave reflection. In individuals above age of 50 years as well as in subjects with long-lasting elevated blood pressure, vascular changes occur predominantly in conduit arteries which become stiffer. Activation of renin-angiotensin-aldosterone and endothelin systems plays a key role in endothelial dysfunction, vascular remodelling, and aging by inducing reactive oxygen species production, and promoting inflammation and cell growth

H.264 sensor aided video encoder for UAV BLOS missions

This paper presents a new low-complexity H.264 encoder, based on x264 implementation, for Unmanned Aerial Vehicles (UAV) applications. The encoder employs a new motion estimation scheme which make use of the global motion information provided by the onboard navigation system. The results are relevant in low frame rate video coding, which is a typical scenario in UAV behind line-of-sight (BLOS) missions

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient

Patients’ Satisfaction by SmileInTM Totems in Radiotherapy: A Two-Year Mono-Institutional Experience

Background: Patient’s satisfaction is recognized as an indicator to monitor quality in healthcare services. Patient-reported experience measures (PREMs) may contribute to create a benchmark of hospital performance by assessing quality and safety in cancer care. Methods: The areas of interest assessed were: patient-centric welcome perception (PCWP), punctuality, professionalism and comfort using the Lean Six Sigma (LSS) methodology. The RAMSI (Radioterapia Amica Mia SmileINTM (SI) My Friend RadiotherapySI), project provided for the placement of SI totems with four push buttons using HappyOrNot technology in a high-volume radiation oncology (RO) department. The SI technology was implemented in the RO department of the Fondazione Policlinico Universitario A. Gemelli IRCCS. SI totems were installed in different areas of the department. The SI Experience Index was collected, analyzed and compared. Weekly and monthly reports were created showing hourly, daily and overall trends. Results: From October 2017 to November 2019, a total of 42,755 votes were recorded: 8687, 10,431, 18,628 and 5009 feedback items were obtained for PCWP, professionalism, punctuality, and comfort, respectively. All areas obtained a SI-approved rate ≥ 8.0 Conclusions: The implementation of the RAMSI system proved to be doable according to the large amount of feedback items collected in a high-volume clinical department. The application of the LSS methodology led to specific corrective actions such as modification of the call-in-clinic system during operations planning. In order to provide healthcare optimization, a multicentric and multispecialty network should be defined in order to set up a benchmar

Restauration d'image fondée sur la théorie de l'information

This thesis addresses informational formulation of image processing problems. This formulation expresses the solution through a minimization of an information-based energy. These energies belong to the nonparametric class in that they do not make any parametric assumption on the underlying data distribution. Energies are expressed directly as a function of the data considered as random variables. However, classical nonparametric estimation relies on fixed-size kernels which becomes less reliable when dealing with high dimensional data. Actually, recent trends in image processing rely on patch-based approaches which deal with vectors describing local patterns of natural images, e. g., local pixel neighbor- hoods. The k-Nearest Neighbors framework solves these difficulties by locally adapting the data distribution in such high dimensional spaces. Based on these premises, we develop new algorithms tackling mainly two problems of image processing: deconvolution and denoising. The problem of denoising is developed in the additive white Gaussian noise (AWGN) hypothesis and successively adapted to no AWGN realm such as digital photography and SAR despeckling. The denoising scheme is also modified to propose an inpainting algorithm.Cette thèse aborde la formulation par la théorie de l’information des problèmes de traitement d’image. Cette formulation exprime la solution au travers de la minimisation d’une énergie. Ces énergies appartiennent à la classe non paramétrique au sens où elles ne font aucune hypothèse paramétrique sur la distribution des données. Les énergies sont exprimées directement en fonction des données considérées comme des variables aléatoires. Toutefois, l’estimation non paramétrique classique repose sur des noyaux de taille fixe moins fiables lorsqu’il s’agit de données de grande dimension. En particulier, des méthodes récentes dans le traitement de l’image dépendent des données de type ”patch” correspondant à des vecteurs de description de modèles locaux des images naturelles, par exemple, les voisinages de pixels. Le cadre des k-plus proches voisins résout ces difficultés en s’adaptant localement à la distribution des données dans ces espaces de grande dimension. Sur la base de ces prémisses, nous développons de nouveaux algorithmes qui s’attaquent principalement à deux problèmes du traitement de l’image : la déconvolution et le débruitage. Le problème de la restauration est développé dans les hypothèses d’un bruit blanc gaussien additif puis successivement adaptés à domaines tels que la photographie numérique et le débruitage d’image radar (SAR). Le schéma du débruitage est également modifié pour définir un algorithme d’inpainting

FORECAST – A cloud-based personalized intelligent virtual coaching platform for the well-being of cancer patients

Well-being of cancer patients and survivors is a challenge worldwide, considering the often chronic nature of the disease. Today, a large number of initiatives, products and services are available that aim to provide strategies to face the challenge of well-being in cancer patients; nevertheless the proposed solutions are often non-sustainable, costly, unavailable to those in need, and less well-received by patients. These challenges were considered in designing FORECAST, a cloud-based personalized intelligent virtual coaching platform for improving the well-being of cancer patients. Personalized coaching for cancer patients focuses on physical, mental, and emotional concerns, which FORECAST is able to identify. Cancer patients can benefit from coaching that addresses their emotional problems, helps them focus on their goals, and supports them in coping with their disease-related stressors. Personalized coaching in FORECAST offers support, encouragement, motivation, confidence, and hope and is a valuable tool for the wellbeing of a patient

A MINIMUM ENTROPY IMAGE DENOISING ALGORITHM Minimizing Conditional Entropy in a New Adaptive Weighted K-th Nearest Neighbor Framework for Image Denoising

In this paper we address the image restoration problem in the variational framework. The focus is set on denoising applications. Natural image statistics are consistent with a Markov random field (MRF) model for the image structure. Thus in a restoration process attention must be paid to the spatial correlation between adjacent pixels.The proposed approach minimizes the conditional entropy of a pixel knowing its neighborhood. The estimation procedure of statistical properties of the image is carried out in a new adaptive weighted k-th nearest neighbor (AWkNN) framework. Experimental results show the interest of such an approach. Restoration quality is evaluated by means of the RMSE measure and the SSIM index, more adapted to the human visual system.

Remote sensing in the fight against environmental crimes: The case study of the cattle-breeding facilities in southern Italy

Enforcement of environmental regulation is a persistent challenge and timely detection of the violations is key to holding the violators accountable. The use of remote sensing data is becoming an effective practice in the fight against environmental crimes. In this work, a novel and effective approach for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multispectral data together with geospatial analyses in the geographic information system (GIS) environment, is proposed. Experiments on data available for the area of Caserta (Southern Italy), show that the proposed technique provides very high detection capability, up to 90%, with a acceptable false alarm rate, becoming a useful tool in the hand of agencies engaged in the protection of territory

Percutaneous real-time sonoelastography as a non-invasive tool for the characterization of solid focal liver lesions: A prospective study

Real-time sonoelastography is currently used for the characterization of superficial solid lesions such as thyroid and breast masses. This study evaluates the usefulness of percutaneous sonoelastography for the characterization of solid focal liver lesions

A nonlocal approach for SAR image denoising

Speckle reduction is a key step in several SAR image processing procedures. In this paper, a new despeckling technique based on the “nonlocal” denoising filter BM3D is presented. The filter has been modified in order to take into account SAR image characteristics. The experimental results, conducted on both synthetic and real SAR images, confirm the potential of the proposed approach