Cultural Impact on Digital Ecosystems: Exploring User Activity in Italy and the USA during the COVID-19 Pandemic

The COVID-19 pandemic significantly impacted people’s lives, leading to an unprecedented amount of data generated on the Internet. In this paper, we present the results of an in-depth analysis of user behavior in the digital ecosystem in Italy and the USA during the first six months of the pandemic. Our objective is to verify whether different cultures have been able to significantly impact the searches carried out by users online and their interactions on social networks

Fyn Mediates Leptin Actions in the Thymus of Rodents

BACKGROUND:Several effects of leptin in the immune system rely on its capacity to modulate cytokine expression and apoptosis in the thymus. Surprisingly, some of these effects are dependent on signal transduction through the IRS1/PI3-kinase, but not on the activation of JAK2. Since all the well known effects of leptin in different cell types and tissues seem to be dependent on JAK2 activation, we hypothesized that, at least for the control of thymic function, another, unknown kinase could mediate the transduction of the leptin signal from the ObR towards the IRS1/PI3-kinase signaling cascade. METHODOLOGY/PRINCIPAL FINDINGS:Here, by employing immunoblot, real-time PCR and flow citometry we show that the tyrosine kinase, Fyn, is constitutively associated with the ObR in thymic cells. Following a leptin stimulus, Fyn undergoes an activating tyrosine phosphorylation and a transient association with IRS1. All these effects are independent of JAK2 activation and, upon Fyn inhibition, the signal transduction towards IRS1/PI3-kinase is abolished. In addition, the inhibition of Fyn significantly modifies the effects of leptin on thymic cytokine expression. CONCLUSION/SIGNIFICANCE:Therefore, in the thymus, Fyn acts as a tyrosine kinase that transduces the leptin signal independently of JAK2 activation, and mediates some of the immunomodulatory effects of leptin in this tissue

Correlation of gene expression and protein production rate - a system wide study

<p>Abstract</p> <p>Background</p> <p>Growth rate is a major determinant of intracellular function. However its effects can only be properly dissected with technically demanding chemostat cultivations in which it can be controlled. Recent work on <it>Saccharomyces cerevisiae </it>chemostat cultivations provided the first analysis on genome wide effects of growth rate. In this work we study the filamentous fungus <it>Trichoderma reesei </it>(<it>Hypocrea jecorina</it>) that is an industrial protein production host known for its exceptional protein secretion capability. Interestingly, it exhibits a low growth rate protein production phenotype.</p> <p>Results</p> <p>We have used transcriptomics and proteomics to study the effect of growth rate and cell density on protein production in chemostat cultivations of <it>T. reesei</it>. Use of chemostat allowed control of growth rate and exact estimation of the extracellular specific protein production rate (SPPR). We find that major biosynthetic activities are all negatively correlated with SPPR. We also find that expression of many genes of secreted proteins and secondary metabolism, as well as various lineage specific, mostly unknown genes are positively correlated with SPPR. Finally, we enumerate possible regulators and regulatory mechanisms, arising from the data, for this response.</p> <p>Conclusions</p> <p>Based on these results it appears that in low growth rate protein production energy is very efficiently used primarly for protein production. Also, we propose that flux through early glycolysis or the TCA cycle is a more fundamental determining factor than growth rate for low growth rate protein production and we propose a novel eukaryotic response to this i.e. the lineage specific response (LSR).</p

Using Social Media for Personalizing the Cultural Heritage Experience

This article presents a personalized recommendation approach of textual and multimedia resources related to artistic and cultural points of interest (POIs). This approach exploits linked open data to retrieve content related to POIs and social media to personalize their recommendation to the target user. The similarity evaluation between the social user profile and the related material occurs based on the classic doc2vec model. A preliminary comparative analysis conducted on 20 real users showed encouraging experimental results in terms of perceived accuracy and beyond-accuracy metrics

Cross-domain recommendation for enhancing cultural heritage experience

In this paper, we describe our research activities for integrating the recommendation process of nearby points of artistic and cultural interest (POIs) with related multimedia content. The recommendation engine exploits the potential offered by linked open data (LOD), by following semantic links in the LOD graph to identify movies, books, and music artists/songs related to that specific POI. This content is subsequently reranked based on the activity of the user and her friends on social media (i.e., Facebook), in order to provide personalized suggestions

Towards Cognitive Modeling of User Needs in Web Browsing Activities

There is an increasing reliance on the Internet for information seeking in many everyday activities. While spending on Internet connectivity has increased, besides traditional IR-based search engines, there is still a lack of tools for helping users searching information in this large, self-organizing and dynamic environment. Very few attempts have been undertaken to better understand the user behaviour, preferences and needs during Web information seeking tasks. Cognitive models, as approximations of cognitive processes able to predict and comprehend basic human tasks, have the chance to provide a deep analysis of the user behaviour during Web search activities. In this paper, we summarize the models recently proposed to address this issue, and we present a novel approach based on the SAM theory bent on the recognition of the user needs during Web browsing activities. The simulation of the human memory processes in terms of learning and recall by means of short and long-term semantic memory structures provides relevant information useful to adapt the traditional HCI in several important domains, such as filtering, retrieval and recommending systems