Selective Exposure shapes the Facebook News Diet

The social brain hypothesis fixes to 150 the number of social relationships we are able to maintain. Similar cognitive constraints emerge in several aspects of our daily life, from our mobility up to the way we communicate, and might even affect the way we consume information online. Indeed, despite the unprecedented amount of information we can access online, our attention span still remains limited. Furthermore, recent studies showed the tendency of users to ignore dissenting information but to interact with information adhering to their point of view. In this paper, we quantitatively analyze users' attention economy in news consumption on social media by analyzing 14M users interacting with 583 news outlets (pages) on Facebook over a time span of 6 years. In particular, we explore how users distribute their activity across news pages and topics. We find that, independently of their activity, users show the tendency to follow a very limited number of pages. On the other hand, users tend to interact with almost all the topics presented by their favored pages. Finally, we introduce a taxonomy accounting for users behavior to distinguish between patterns of selective exposure and interest. Our findings suggest that segregation of users in echo chambers might be an emerging effect of users' activity on social media and that selective exposure -- i.e. the tendency of users to consume information interest coherent with their preferences -- could be a major driver in their consumption patterns.Comment: PLOS One Published: March 13, 202

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model

Motivation: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher.Results: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms.Availability and implementation: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github. com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380

Impact of engineered nanoparticles in initiating or modulating pathology-related Inflammation

The possibility that nanomaterials could perturb the normal course of an inflammatory response is a key issue when assessing nano-immunosafety. The alteration of the normal progress of an inflammatory response may have pathological consequences, since inflammation is a major defensive mechanism and its efficiency maintains the body’s health. We can thus consider as pathology-related inflammation those inflammatory reactions that, instead of eliminating foreign agents, lack down-regulation and cause tissue damage. To assess the ability of nanoparticles to initiate and modulate inflammatory reactions, an in vitro model was used that recapitulates all the stages of infection-induced inflammation, from initiation to resolution, based on human primary blood monoytes. A parallel model reproducing pathological chronic inflammation shows that the differences between resolving and persistent inflammation are subtle and evident only upon kinetic analysis of gene expression profiles and production of inflammatory factors. Rigorously endotoxin-free Au and Ag nanoparticles have been assessed for their ability to directly initiate in vitro inflammation and for their capacity to modulate the course both physiological resolving inflammation and pathological persistent inflammation. In no case significant effects were observed, with the exception of a transient increase of the inflammatory response in the presence of Ag nanoparticles. An important issue in the regulation of monocyte/macrophage inflammatory functions is the capacity of innate “memory”, i.e., the ability of respond differently to a challenge if previously primed with the same or a different agent. How nanoparticles can impact innate memory was assessed by using Au nanoparticles as priming and challenge agent with and without LPS and zymosan. Priming with LPS and zymosan could drastically decrease the response of monocytes (production of TNFa) to a challenge with any stimulus, given 7 days after the first. The presence of Au nanoparticles did not influence such behaviour. Likewise, Au nanoparticles did not directly induce memory, i.e., did not influence the response of monocytes to subsequent stimuli. We conclude that Au and Ag nanoparticles, at the size and concentrations used, are taken up by monocytes without this causing any notable interference with their capacity to mount an adequate defensive responses to microbial challenges, either immediate or after some time from exposure. This work was supported by per EU FP7 projects HUMUNITY and BioCog, the H2020 project PANDORA, the CNR Flagship Project InterOmics, and the cluster project Medintech of the Italian Ministry of Education, University and Research

Redescription of <i>Cercopithifilaria bainae</i> Almeida &amp; Vicente, 1984 (Spirurida, Onchocercidae) from a dog in Sardinia, Italy

Background Three species of the genus Cercopithifilaria have been morphologically and molecularly characterized in dog populations in southern Europe: Cercopithifilaria grassii (Noè, 1907), Cercopithifilaria sp. sensu Otranto et al., 2011 (reported as Cercopithifilaria sp. I), and Cercopithifilaria sp. II sensu Otranto et al., 2012. The adults of Cercopithifilaria sp. I have remained unknown until the present study. Methods The material originated from a dog from Sardinia (Italy) diagnosed with dermal microfilariae of Cercopithifilaria sp. I. The holotype and three paratypes of Cercopithifilaria bainae Almeida &amp; Vicente, 1984, described from dogs in Brazil, were studied as comparative material. A cox1 (~689 bp) and 12S (~330 bp) gene fragments were amplified and phylogenetic analysis carried out. Results The highest numbers of adult nematodes (82%) were collected in the sediment of the subcutaneous tissues of the trunk (n = 37) and forelimbs (n = 36). The morphology of the adult nematodes and microfilariae collected from the dog in Sardinia corresponded to those of C. bainae. All cox1 and 12S gene sequences showed a high homology (99-100%) with sequences from microfilariae of Cercopithifilaria sp. I. Conclusions The morphological and molecular identity of the microfilariae of C. bainae overlap those described previously as Cercopithifilaria sp. sensu Otranto et al., 2011 (=Cercopithifilaria sp. I). Therefore, the present study reports the occurrence of C. bainae in Europe, for the first time after its description and the single record in Brazil. C. bainae appears to be highly diffused in dog populations in southern Europe. The phylogenetic analyses based on cox1 and 12S do not reveal the three species of Cercopithifilaria parasitizing dogs as a monophyletic group, which suggests that they have derived independently by host switching

Importance of earthquake rupture geometry on tsunami modelling: the Calabrian Arc subduction interface (Italy) case study

SUMMARY The behaviour of tsunami waves at any location depends on the local morphology of the coasts, the encountered bathymetric features, and the characteristics of the source. However, the importance of accurately modelling the geometric properties of the causative fault for simulations of seismically induced tsunamis is rarely addressed. In this work, we analyse the effects of using two different geometric models of the subduction interface of the Calabrian Arc (southern Italy, Ionian Sea) onto the simulated tsunamis: a detailed 3-D subduction interface obtained from the interpretation of a dense network of seismic reflection profiles, and a planar interface that roughly approximates the 3-D one. These models can be thought of as representing two end-members of the level of knowledge of fault geometry. We define three hypothetical earthquake ruptures of different magnitudes (Mw 7.5, 8.0, 8.5) on each geometry. The resulting tsunami impact is evaluated at the 50-m isobath in front of coastlines of the central and eastern Mediterranean. Our results show that the source geometry imprint is evident on the tsunami waveforms, as recorded at various distances and positions relative to the source. The absolute differences in maximum and minimum wave amplitudes locally exceed one metre, and the relative differences remain systematically above 20 per cent with peaks over 40 per cent. We also observe that tsunami energy directivity and focusing due to bathymetric waveguides take different paths depending on which fault is used. Although the differences increase with increasing earthquake magnitude, there is no simple rule to anticipate the different effects produced by these end-member models of the earthquake source. Our findings suggest that oversimplified source models may hinder our fundamental understanding of the tsunami impact and great care should be adopted when making simplistic assumptions regarding the appropriateness of the planar fault approximation in tsunami studies. We also remark that the geological and geophysical 3-D fault characterization remains a crucial and unavoidable step in tsunami hazard analyses

Time, Space and Social Interactions: Exit Mechanisms for the Covid-19 Epidemics

We develop a minimalist compartmental model to study the impact of mobility restrictions in Italy during the Covid-19 outbreak. We show that an early lockdown shifts the epidemic in time, while that beyond a critical value of the lockdown strength, the epidemic tend to restart after lifting the restrictions. As a consequence, specific mitigation strategies must be introduced. We characterize the relative importance of different broad strategies by accounting for two fundamental sources of heterogeneity, i.e. geography and demography. First, we consider Italian regions as separate administrative entities, in which social interactions between age classs occur. Due to the sparsity of the inter-regional mobility matrix, once started the epidemics tend to develop independently across areas, justifying the adoption of solutions specific to individual regions or to clusters of regions. Second, we show that social contacts between age classes play a fundamental role and that measures which take into account the age structure of the population can provide a significant contribution to mitigate the rebound effects. Our model is general, and while it does not analyze specific mitigation strategies, it highlights the relevance of some key parameters on non-pharmaceutical mitigation mechanisms for the epidemics

Characterizing Engagement Dynamics across Topics on Facebook

Social media platforms heavily changed how users consume and digest information and, thus, how the popularity of topics evolves. In this paper, we explore the interplay between the virality of controversial topics and how they may trigger heated discussions and eventually increase users' polarization. We perform a quantitative analysis on Facebook by collecting $\sim57M$ posts from $\sim2M$ pages and groups between 2018 and 2022, focusing on engaging topics involving scandals, tragedies, and social and political issues. Using logistic functions, we quantitatively assess the evolution of these topics finding similar patterns in their engagement dynamics. Finally, we show that initial burstiness may predict the rise of users' future adverse reactions regardless of the discussed topic