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Fuel management is a crucial action to maintain wildland fires under the threshold of manageability; hence, in order to allocate resources in the best way, wildland fuel mapping is regarded as a necessary tool by land managers. Several studies have used Aerial Laser Scanner (ALS) data to estimate forest fuels characteristics at plot level, but few have extended such estimates at a zonal level.In the context of the EU Interreg Project CROSSIT SAFER, a test of the possibilities of ALS data to predict fuels attributes has been performed in three different areas: an alpine basin, a coastal wildland-urban interface and a karstic highland. Eighteen sampling plots have been laid out over 6 forest categories, with a special focus on Pinus nigra J. F. Arnold artificial forests. Low density (average 4 points/m2) discrete return LiDAR data has been analysed with FUSION, a free point cloud analysis software tailored to forestry purposes; field and remote sensing data have been connected with simple statistical modelling and results have been spatialised over the case study areas to provide wall-to-wall inputs for FLAMMAP fire behaviour simulation software.Resulting maps can be of relevance for land managers to better highlight the most vulnerable or fire prone areas at a mesoscale administrative level. Limitations and room for improvement are pointed out, in the view that land management should keep updated with the latest technology available.

The fetal brain undergoes extensive morphological changes throughout pregnancy, which can be visually seen in ultrasound acquisitions. We explore the use of convolutional neural networks (CNNs) for the segmentation of multiple fetal brain structures in 3D ultrasound images. Accurate automatic segmentation of brain structures in fetal ultrasound images can track brain development through gestation, and can provide useful information that can help predict fetal health outcomes. We propose a multi-task CNN to produce automatic segmentations from atlas-generated labels of the white matter, thalamus, brainstem, and cerebellum. The network as trained on 480 volumes produced accurate 3D segmentations on 48 test volumes, with Dice coefficient of 0.93 on the white matter and over 0.77 on segmentations of thalamus, brainstem and cerebellum.

For many medical applications, large quantities of imaging data are routinely obtained but it can be difficult and time-consuming to obtain high-quality labels for that data. We propose a novel uncertainty-based method to improve the performance of segmentation networks when limited manual labels are available in a large dataset. We estimate segmentation uncertainty on unlabeled data using test-time augmentation and test-time dropout. We then use uncertainty metrics to select unlabeled samples for further training in a semi-supervised learning framework. Compared to random data selection, our method gives a significant boost in Dice coefficient for semi-supervised volume segmentation on the EADC-ADNI/HARP MRI dataset and the large-scale INTERGROWTH-21st ultrasound dataset. Our results show a greater performance boost on the ultrasound dataset, suggesting that our method is most useful with data of lower or more variable quality.

To demonstrate the potential of azido-substituted carbon nanotubes for application in nanomedicine, multiple-decorated oxidized multi-walled carbon nanotubes as drug delivery systems have been synthesized. These DDSs were able to carry doxorubicin inside breast MCF-7 cancer cell lines resulting in an enhanced cytotoxic effect with respect to the free drug. Decoration of the carbon nanotubes was accomplished through both covalent and non-covalent approaches: versatile click reactions and π–π interactions were exploited. To assess the internalization of the carbon nanotubes inside cells, decoration with a BODIPY fluorescent molecule was performed. Furthermore, the nanotubes were decorated with a biotin selector molecule to increase the uptake of the system by cancer cells. Comparative studies were performed on the complete drug delivery system to highlight its effect with respect to the free drug and the contribution of the selector in the internalization efficiency. Finally, preliminary in vivo tests were performed on MCF-7 inoculated mice. A net improvement in efficiency, concerning the minor growth of the tumors, has been found when using doxorubicin loaded on our drug delivery system with respect to free doxorubicin.

This study compares child poverty in seven European countries—Germany, Denmark, Belgium, France, the UK, Italy, and Spain, between 1994 and 2000, identifying both macro- and the micro-level conditions thought to contribute to child poverty in each of the countries. The paper analyses correlates of child poverty in the chosen countries and offers comparative analyses across countries and regions of the European Union. In doing so, I try to build up a multilevel theory of child poverty, i.e. a micro–macro theory in which the levels interact with each other. This is done via the so-called Coleman’s boat. Within such a framework, the concept of endowment is formulated and applied to account for child poverty risks. The parents’ endowment is defined as a set of material and nonmaterial resources transmitted by the macro environment in which they live, accumulated or used by them in the past, made available as well as unavailable by other individuals’ actions and mediated by the individuals’ desires and beliefs. I claim that parental and household endowments play a major role in explaining child poverty risks, particularly if we assume that a child is poor when he or she lives in a poor household. Parents with access to a strong endowment can resort to such resources to prevent the risk of poverty for their dependent children. On the contrary, weak endowments may expose the children to higher poverty risks. We find that in Denmark, for example, the macro-level resources come into play at the micro-level and mediate the relation between child poverty risks as a function of parents’ endowments and well-being outcomes. As a result, the macro structure of social risks is not reproduced in the configuration of well-being macro outcomes. In Italy and Spain, on the other hand, evidence is stronger for an intergenerational transmission of poverty.

A series of azido-dyes were synthesized through Knoevenagel reactions of an azido-BODIPY with aromatic aldehydes. The nature of the substituents allowed the fine tuning of their spectroscopic properties. The dyes were used to decorate oxidized multiwalled carbon nanotubes (ox-MWCNTs), bearing terminal triple bond groups, by CuAAC reactions, affording fluorescent materials. This decoration allowed the efficient determination of the internalization of the ox-MWCNT derivatives by different model cancer cells, such as MCF7.

Socioeconomic inequality and its impact on health is a growing concern in the European public health debate. In many countries, the issue is moving away from description towards the identification of the determinants of inequalities and the development of policies explicitly aimed at reducing inequalities in health. In Italy, ten years after the publication of the first report on inequalities in health, this topic is seldom present on the agenda of public policy makers. The purpose of this report is to update the Italian profile of social variation in health and health care in order to stimulate the debate on ways to tackle inequalities in health that are preventable. In the first section of this book, the threefold objective is to describe the principal mechanisms involved in the generation of social inequalities in health (Introduction); to report Italian data on the distribution and magnitude of this phenomenon in the last decade; and to evaluate policies and interventions in both the social (chapter 1.9, Section I) and the health sector (chapter 2.3, Section I), which are potentially useful to reduce health inequalities. It is intended for anyone who is in a position to contribute t o decision-making that will benefit the health of communities. For this reason, chapters are organized by specific determinants of inequalities on which interentions may have an impact. The methodological approach in the second section focuses on the best methods to monitor social inequalities including recommendations on social indicators, sources of information and study models, based on European guidelines revised for the Italian situation. According to data from national and local studies, mortality increases linearly with social disadvantage for a wide range of indicators at both the individual (education, social class, income, quality of housing) and the geographical level (deprivation indexes computed at different levels of aggregation). This positive correlation is evident for both sexes, with the steepest gradient observed among adults of working age, although differences persist also among the elderly. The causes of death found to be most highly correlated with social inequality, and largely responsible for the increasing inequality over the last decade, are those associated with addiction and exclusion (drug, alcohol and violence related deaths), with smoking (lung cancer) and with safety in the workplace and on the roads (accidents). Similar gradients and trends have been observed with different outcomes, such as self-reported morbidity, disability and cancer incidence (chapter 1.1, Section I). Reproductive outcomes confiirm this picture: compared to women belonging to the upper classes, those women in low conditions experience more spontaneous abortions and their children suffer from higher infant mortality and low birth weight. This is a critical issue since poor infant health, particularly for metabolic and respiratory pathologies, affects health in adult life. There is now substantive evidence showing that also socioeconomic circumstances at birth or during adolescence may have a strong impact on adult health (chapter 1.2, Section I). Differences in harmful lifestyles, such as smoking, heavy drinking, drug use, unhealthy diet, obesity and physical inactivity, have a similar effect. The only exception is smoking among women, which is positively correlated with socioeconomic status; however, since women in the upper classes have a greater tendency to quit smoking, the gradient will soon be reversed (chapter 1.7, Section I). On the other hand, most of these behaviours do not follow from free and conscious individual choice; they are a form of adaptation to chronic stress originating in the work-place (chapter 1.4, Section I), or to particularly unfavourable events and conditions, such as unemployment (chapter 1.5, Section I) or lack of family and social support (chapter 1.6, Section I). Poor socioeconomic circumstances are the threshold of absolute poverty and may lead to social exclusion, a condition with a heavy impact on health, which in Italy includes marginal groups of the native population and broader classes of immigrants (chapter 1.3, Section I). Finally, there is recent and consistent evidence on the existence of a "contextual" effect on health, as opposed to the "compositional" effect given solely by the aggregation of individual processes. According to this hypothesis, characteristics of the infrastructure, and the physical and socioeconomic environment of an area would have an impact on individual health independent from the cultural and economic resources personally available to people living in that area (chapter 1.8, Section I). With respect to the health care system, various studies are in agreement in demonstrating that poor and less educated people have inadequate access both to primary prevention and early diagnosis (chapter 2.1, Section I), and to early and appropriate care (chapter 2.2, Section I). They also experience higher rates of hospitalization, particularly in emergencies and with advanced levels of severity.