The temporal communication patterns of human individuals are known to be inhomogeneous or bursty, which is reflected as heavy tail behavior in the inter-event time distribution. As the cause of such a bursty behavior two main mechanisms have been suggested: (i) inhomogeneities due to the circadian and weekly activity patterns and (ii) inhomogeneities rooted in human task execution behavior. In this paper, we investigate the role of these mechanisms by developing and then applying systematic de-seasoning methods to remove the circadian and weekly patterns from the time series of mobile phone communication events of individuals. We find that the heavy tails in the interevent time distributions remain robust with respect to this procedure, which clearly indicates that the human task execution-based mechanism is a possible cause of the remaining burstiness in temporal mobile phone communication patterns.

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Treating the hypoxic region of the tumor remains a significant challenge. The goals of this study are to develop an exosome platform that can target regions of tumor hypoxia and that can be monitored in vivo using magnetic particle imaging (MPI). Four types of exosomes (generated under hypoxic or normoxic conditions, and with or without exposure to X-ray radiation) were isolated from MDA-MB-231 human breast cancer cells. Exosomes were labeled by DiO, a fluorescent lipophilic tracer, to quantify their uptake by hypoxic cancer cells. Subsequently, the exosomes were modified to carry SPIO (superparamagnetic iron oxide) nanoparticles and Olaparib (PARP inhibitor). FACS and fluorescence microscopy showed that hypoxic cells preferentially take up exosomes released by hypoxic cells, compared with other exosome formulations. In addition, the distribution of SPIO-labeled exosomes was successively imaged in vivo using MPI. Finally, the therapeutic efficacy of Olaparib-loaded exosomes was demonstrated by increased apoptosis and slower tumor growth in vivo. Our novel theranostic platform could be used as an effective strategy to monitor exosomes in vivo and deliver therapeutics to hypoxic tumors.

The friendship paradox states that your friends have on average more friends than you have. Does the paradox “hold” for other individual characteristics like income or happiness? To address this question, we generalize the friendship paradox for arbitrary node characteristics in complex networks. By analyzing two coauthorship networks of Physical Review journals and Google Scholar profiles, we find that the generalized friendship paradox (GFP) holds at the individual and network levels for various characteristics, including the number of coauthors, the number of citations, and the number of publications. The origin of the GFP is shown to be rooted in positive correlations between degree and characteristics. As a fruitful application of the GFP, we suggest effective and efficient sampling methods for identifying high characteristic nodes in large-scale networks. Our study on the GFP can shed lights on understanding the interplay between network structure and node characteristics in complex networks.