Expression and localization of NUB1 in Tauopathy and Alzheimer’s disease models

Alzheimer’s disease (AD) is characterized at a subcellular level by intracellular neurofibrillary tangles (NFTs), aggregates of hyperphosphorylated Tau, and by senile plaques, extracellular aggregates of amyloid beta peptides. Previous data revealed that Nedd8 ultimate buster 1 (NUB1) plays a role in reducing the aggregation and phosphorylation of Tau in an in vitro model. To clarify the role of NUB1 in AD, the spatiotemporal expression and localization of NUB1 was analyzed in T301L, a mouse model for Tauopathy characterized by NFTs, and in TASTPM mice, characterized by early development of senile plaques. The analysis revealed no change in the level of NUB1 expression in T301L mice and a significant decrease at 12 months in TASTPM mice. In brain cryosections, NUB1 expression was detected in the hippocampus and entorhinal cortex. Subcellularly, NUB1 was localized predominantly in the neuronal nuclei, but also in neuronal processes. In both mouse models at 12 months, NUB1 signal was observed to co-localize with AT8 positive cytoplasmic aggregates. Moreover in TASTPM mice, a NUB1 cytoplasmic signal was observed in non-neuronal cells. Our data confirm that NUB1 could be a therapeutic target in AD and also help to establish the appropriate window of opportunity for therapeutic intervention

A deep learning approach for detecting security attacks on blockchain

In these last years, Blockchain technologies have been widely used in several application fields to improve data privacy and trustworthiness and security of systems. Although the blockchain is a powerful tool, it is not immune to cyber attacks: for instance, recently (January 2019) a successful 51% attack on Ethereum Classic has revealed security vulnerabilities of its platform. Under a statistical perspective, attacks can be seen as an anomalous observation, with a strong deviation from the regular behavior. Machine Learning is a science whose goal is to learn insights, patterns and outliers within large data repositories; hence, it can be exploit for blockchain attack detection. In this work, we define an anomaly detection system based on a encoder-decoder deep learning model, that is trained exploiting aggregate information extracted by monitoring blockchain activities. Experiments on complete historical logs of Ethereum Classic network prove the capability of the our model to effectively detect the publicly reported attacks. To the best of our knowledge, our approach is the first one that provides a comprehensive and feasible solution to monitor the security of blockchain transactions

Fighting Misinformation, Radicalization and Bias in Social Media

Social media have become the ideal place for black hats and malicious individuals to target susceptible users through different attack vectors and then manipulate their opinions and interests. Fake news, radicalization, and pushing bias into the data represent some popular ways noxious users adopt to perpetrate their criminal intents. In this evolving scenario, Artificial Intelligence techniques represent a valuable tool to early detect and mitigate the risk due to the spreading of these emerging attacks. In this work, we describe the Machine Learning based solutions developed to address the problems mentioned above and our current research

Productive integration, economic recession and employment in Europe: an assessment based on vertically integrated sectors

The Covid-19 crisis has revamped the discussion about the redefinition of GVC. This paper contributes to the debate, analysing the productive relationships between European countries in four key manufacturing activities. In particular, the paper addresses two objectives. First, it maps the degree of productive integration in Europe, focusing on the generation of employment in the production of exported intermediate inputs and final goods. Second, it provides a preliminary assessment of the potential impact on employment that the current economic crisis will have on some manufacturing activities across Europe. The analysis is realised employing the concept of vertically integrated labour (Pasinetti 1973) which allows to account for the employment directly and indirectly involved in the production of final goods. The estimations are derived from Multi-Regional Input–Output tables to map the supply chain and to differentiate between the employment involved in the production of exported intermediate inputs and final goods. The results show that most of the employment involved in the production of final output of the activities studied in the paper is linked to international trade. Although Europe shows a high degree of productive links, there are important differences in the modality of insertion in the productive structure of European countries. Moreover, the impact on the level of employment due to the current economic crisis can be significant, affecting more than 1.3 million of people in Europe. These results are relevant to policy makers, who should consider carefully the high degree of linkages of the European economies when designing industrial policies and measure of support to the economy

Topological analysis of migration flows (the case of political refugees)

In this paper, using complex network theory we analyse the problem of illegal immigration on a worldwide scale. This approach appears to be appropriate in view of the invariance of scale property that we found in real data. The presented networks have been obtained using publicly available data collected from 2000 to 2011 by the UNHCR database (asylum-seekers) which is reasonably reliable, complete and regularly updated. Preliminarily, we show the roles played by a number of key nodes and clusters. Then, to find 'important' nodes, we use a role definition which is a variant of that introduced by Guimerà and Amaral (the so-called cartography approach). Finally, a coloured cartography of the clusters obtained through a geopolitical map of the world provides an easy-to-read visual tool to highlight results in a way also suitable to a broader non-technical audienc

Heterogeneity in the demand-growth relationship at the firm level: the role of demand sources and innovation/knowledge characteristics

This work investigates whether different demand sources (i.e. demand for the firms’ output from households, other firms and the public sector) have different effects on firms’ employment growth and whether the growth effects of the demand sources vary by the firms’ innovation/knowledge characteristics. Relying on a representative sample of Italian companies observed between 2012 and 2017, we find that companies serving prevalently other firms or the government as their main demand source tend to grow faster than firms selling final goods to households. However, the growth advantage is more robust for firms serving prevalently other firms as their main demand source. We also find that the relative growth advantage is more pronounced among innovation-intensive and knowledge-intensive firms supplying other firms prevalently. Our findings are robust to the inclusion of firm-level controls and time, sectoral and geographical dummies. Confirming one of the major insights of both the Keynesian and the Schumpeterian traditions, demand emerges as a key driver of growth, the effects of which are mediated by the firms’ innovation/knowledge characteristics

Technological sovereignty and strategic dependencies: The case of the photovoltaic supply chain

This study investigates the international Photovoltaic Supply Chain (PVSC) from the perspective of the recent literature on Strategic Dependencies and Technological Sovereignty. In so doing, the paper fills existing literature gaps by providing: i) a fine-grained long-term analysis of the PVSC, considering all its segments and integrating production and technology dimensions; ii) detailed evidence of the changing hierarchical relationships within the supply chain; iii) an analysis of the drivers of strategic dependencies in the PVSC in different economic areas. Focusing on China, the EU, Japan, South Korea, and the US over the 2007–2021 period, the empirical evidence highlights the existence of strong strategic dependencies in the EU, especially in the mid and downstream segments of the PVSC. A similar, and in some cases even worse, situation is found in the US, although some diversification of the portfolio of suppliers, especially in the downstream, has been emerging in more recent years. These results have relevant implications in terms of policy theory with specific reference to EU and US initiatives in the field of industrial policies for sustainable transition