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Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency.

Pneumocystis carinii pneumonia is an oftentimes fatal infection for hosts in an immunocompromised state. The disease occurs in a wide variety of mammals, but the etiologic agent of this disease has been referred to as P. carinii regardless of the host species. However, even within a single host species, such as laboratory rats, distinct varieties of P. carinii have been identified from differences in the electrophoretic migration of chromosomes in agarose gels. Here we present evidence indicating that some laboratory rats can contain two different genetic variants of P. carinii that differ not only in electrophoretic karyotype but also in the presence of a particular repeated DNA sequence, in the presence of an intron in the 18S ribosomal RNA gene, and in the sequence of part of the 18S rRNA gene. Most of the rat colonies studied were infected with P. carinii that contained the repeated DNA and the 18S rRNA gene intron. The other type of rat-derived P. carinii, which lacked the repeated DNA and the intron in the 18S rRNA gene, was found as a coinfection with the first. Parasite populations from different coinfected rats contained the two variants in different proportions.

The ability of the brain to predict future events based on the pattern of recent sensory experience is critical for guiding animal's behavior. Neocortical circuits for ongoing processing of sensory stimuli are extensively studied, but their contributions to the anticipation of upcoming sensory stimuli remain less understood. We, therefore, used in vivo cellular imaging and fiber photometry to record mouse primary auditory cortex to elucidate its role in processing anticipated stimulation. We found neuronal ensembles in layers 2/3, 4, and 5 which were activated in relationship to anticipated sound events following rhythmic stimulation. These neuronal activities correlated with the occurrence of anticipatory motor responses in an auditory learning task. Optogenetic manipulation experiments revealed an essential role of such neuronal activities in producing the anticipatory behavior. These results strongly suggest that the neural circuits of primary sensory cortex are critical for coding predictive information and transforming it into anticipatory motor behavior.

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