Sibutramine-dependent brown fat activation in rats: an immunohistochemical study

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Road pavement crack automatic detection by MMS images

none4noThe research topic was to test different feature extraction methods to localize road pavement cracks useful to construct a spatial database for the pavement distress monitoring. Several images were acquired by means of a line scan camera that assembled in a Mobile Mapping System (MMS) allows tracking directly the position of the images by a GPS-INS system. Following an automatic digital image processing was performed by means of several algorithms based on different approaches (edge detection and fuzzy set theory). The detected cracks were described with some parameters in relation to some shape characteristics (dimension, typology, direction), which are necessary to recognize the gravity of the road pavement conditions. The edge detection techniques tested in this research allowed identifying fatigue cracking or alligator cracking and also thin linear cracks in images with strong radiometric jumps by applying filters, gradient functions and morphological operators. The snake approach was one of them, in particular the type called Gradient Vector Flow (GVF). Another approach was based on the fuzzy theory. The advantage of this method is that the pixels, necessary to identify the cracks in road pavement, are darker than their surroundings in an image. The last stage was the pavement distress spatial database collection. The Mobile Mapping System (MMS) has allowed localizing the raster data and consequently the vector features of the detected cracks, associating into the table their attributes too. The proposed approaches allow to automatically localize and classify the kind of road pavement crack.Automatic Detection, Feature extraction methods, Gradient function, Gradient vector flow, Line-scan cameras, Mobile mapping systems, Morphological operator, Shape characteristicsA. Mancini;E. S. Malinverni;E. Frontoni;P. ZingarettiMancini, Adriano; Malinverni, Eva Savina; Frontoni, Emanuele; Zingaretti, Prim

Supervised cnn strategies for optical image segmentation and classification in interventional medicine

The analysis of interventional images is a topic of high interest for the medical-image analysis community. Such an analysis may provide interventional-medicine professionals with both decision support and context awareness, with the final goal of improving patient safety. The aim of this chapter is to give an overview of some of the most recent approaches (up to 2018) in the field, with a focus on Convolutional Neural Networks (CNNs) for both segmentation and classification tasks. For each approach, summary tables are presented reporting the used dataset, involved anatomical region and achieved performance. Benefits and disadvantages of each approach are highlighted and discussed. Available datasets for algorithm training and testing and commonly used performance metrics are summarized to offer a source of information for researchers that are approaching the field of interventional-image analysis. The advancements in deep learning for medical-image analysis are involving more and more the interventional-medicine field. However, these advancements are undeniably slower than in other fields (e.g. preoperative-image analysis) and considerable work still needs to be done in order to provide clinicians with all possible support during interventional-medicine procedures

A cloud-based healthcare infrastructure for neonatal intensive-care units

Intensive medical attention of preterm babies is crucial to avoid short-term and long- term complications. Within neonatal intensive care units (NICUs), cribs are equipped with electronic devices aimed at: monitoring, administering drugs and supporting clinician in making diagnosis and offer treatments. To manage this huge data flux, a cloud-based healthcare infrastructure that allows data collection from different devices (i.e., patient monitors, bilirubinometers, and transcutaneous bilirubinometers), storage, processing and transferring will be presented. Communication protocols were designed to enable the communication and data transfer between the three different devices and a unique database and an easy to use graphical user interface (GUI) was implemented. The infrastructure is currently used in the “Women’s and Children’s Hospital G.Salesi” in Ancona (Italy), supporting clinicians and health opertators in their daily activities

DEEP CONVOLUTIONAL NEURAL NETWORKS FOR SENTIMENT ANALYSIS OF CULTURAL HERITAGE

Abstract. The promotion of Cultural Heritage (CH) goods has become a major challenges over the last years. CH goods promote economic development, notably through cultural and creative industries and tourism. Thus, an effective planning of archaeological, cultural, artistic and architectural sites within the territory make CH goods easily accessible. A way of adding value to these services is making them capable of providing, using new technologies, a more immersive and stimulating fruition of information. In this light, an effective contribution can be provided by sentiment analysis. The sentiment related to a monument can be used for its evaluation considering that if it is positive, it influences its public image by increasing its value. This work introduces an approach to estimate the sentiment of Social Media pictures CH related. The sentiment of a picture is identified by an especially trained Deep Convolutional Neural Network (DCNN); aftewards, we compared the performance of three DCNNs: VGG16, ResNet and InceptionResNet. It is interesting to observe how these three different architectures are able to correctly evaluate the sentiment of an image referred to a ancient monument, historical buildings, archaeological sites, museum objects, and more. Our approach has been applied to a newly collected dataset of pictures from Instagram, which shows CH goods included in the UNESCO list of World Heritage properties.</p

Sharing health data among general practitioners: The Nu.Sa. project

Today, e-health has entered the everyday work flow in the form of a variety of healthcare providers. General practitioners (GPs) are the largest category in the public sanitary service, with about 60,000 GPs throughout Italy. Here, we present the Nu.Sa. project, operating in Italy, which has established one of the first GP healthcare information systems based on heterogeneous data sources. This system connects all providers and provides full access to clinical and health-related data. This goal is achieved through a novel technological infrastructure for data sharing based on interoperability specifications recognised at the national level for messages transmitted from GP providers to the central domain. All data standards are publicly available and subjected to continuous improvement. Currently, the system manages more than 5,000 GPs with about 5,500,000 patients in total, with 4,700,000 pharmacological e-prescriptions and 1,700,000 e-prescriptions for laboratory exams per month. Hence, the Nu.Sa. healthcare system that has the capacity to gather standardised data from 16 different form of GP software, connecting patients, GPs, healthcare organisations, and healthcare professionals across a large and heterogeneous territory through the implementation of data standards with a strong focus on cybersecurity. Results show that the application of this scenario at a national level, with novel metrics on the architecture's scalability and the software's usability, affect the sanitary system and on GPs’ professional activities

Suspected breast implant rupture: our experience, recommendations on its management and a proposal for a model of informed consent

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