Abstract-Suppose that a rumor originating from a single source among a set of suspects spreads in a network, how to root out this rumor source? With the a priori knowledge of the set of suspect nodes and a snapshot observation of infected nodes, we construct a maximum a posteriori (MAP) estimator to identify the rumor source using the susceptible-infected (SI) model. When analyzing the performance of the MAP estimator, the a priori suspect set and its associated connectivity in the network bring about new ingredients to the problem. For this purpose, we propose to use local rumor center, which is a generalized concept based on the notion of rumor centrality, to identify the source from suspects. For regular tree-type networks with node degree δ, we characterize P c (n), the correct detection probability of the source estimator upon observing n infected nodes, in both the finite and asymptotic regimes. First, when the suspect set degenerates into the entirety of the network, so that every infected node belongs to the suspect set, lim n→∞ P c (n) grows from 0.25 to 0.307 as δ increases from three to infinity, a result first established in Zaman (2011, 2012) via a different approach with more mathematical machinery; furthermore, P c (n) monotonically decreases with n and increases with δ even in the finite-n regime. Second, when the suspect nodes form a connected subgraph of the network, lim n→∞ P c (n) significantly exceeds the a priori probability if δ ≥ 3, and reliable detection is achieved as δ becomes sufficiently large; furthermore, P c (n) monotonically decreases with n and increases with δ. Third, when there are only two suspect nodes, lim n→∞ P c (n) is at least 0.75 if δ ≥ 3; and P c (n) increases with the distance between the two suspects. Fourth, when there are multiple suspect nodes, among all possible connection patterns, that all the suspects form a single connected subgraph of the network achieves the smallest detection probability for the MAP source estimator. Our analysis leverages ideas from the Pólya's urn model in probability theory and sheds insight into the behavior of the rumor spreading process not only in the asymptotic regime but also for the general finite-n regime.

This paper addresses the problem of a single rumor source detection with multiple observations, from a statistical point of view of a spreading over a network, based on the susceptibleinfectious model. For tree networks, multiple sequential observations for one single instance of rumor spreading cannot improve over the initial snapshot observation. The situation dramatically improves for multiple independent observations. We propose a unified inference framework based on the union rumor centrality, and provide explicit detection performance for degree-regular tree networks. Surprisingly, even with merely two observations, the detection probability at least doubles that of a single observation, and further approaches one, i.e., reliable detection, with increasing degree. This indicates that a richer diversity enhances detectability. For general graphs, a detection algorithm using a breadth-first search strategy is also proposed and evaluated. Besides rumor source detection, our results can be used in network forensics to combat recurring epidemic-like information spreading such as online anomaly and fraudulent email spams.

Frequency-dependent learning has been achieved using semiconducting polymer/electrolyte composite cells. The cells composed of polymer/electrolyte double layers realized the conventional spike-rate-dependent plasticity (SRDP) learning model. These cells responded to depression upon low-frequency stimulation and to potentiation upon high-frequency stimulation and presented long-term memory. The transition threshold θm from depression to potentiation varied depending on the previous stimulations. A nanostructure resembling a bio-synapse in its transport passages was demonstrated and a random channel model was proposed to describe the ionic kinetics at the polymer/electrolyte interface during and after stimulations with various frequencies, accounting for the observed SRDP.

Phospholipid transfer protein (PLTP) plays an important role in regulation of inflammation. Previously published studies have shown that PLTP binds, transfers and neutralizes bacterial lipopolysaccharides. In the current study we tested the hypothesis that PLTP can also regulate anti-inflammatory pathways in macrophages. Incubation of macrophage-like differentiated THP1 cells and human monocyte-derived macrophages with wild-type PLTP in the presence or absence of tumor necrosis factor alpha (TNFα) or interferon gamma (IFNγ) significantly increased nuclear levels of active signal transducer and activator of transcription 3, pSTAT3Tyr705 (p<0.01). Similar results were obtained in the presence of a PLTP mutant without lipid transfer activity (PLTPM159E), suggesting that PLTP-mediated lipid transfer is not required for activation of the STAT3 pathway. Inhibition of ABCA1 by chemical inhibitor, glyburide, as well as ABCA1 RNA inhibition, reversed the observed PLTP-mediated activation of STAT3. In addition, PLTP reduced nuclear levels of active nuclear factor kappa-B (NFκB) p65 and secretion of pro-inflammatory cytokines in conditioned media of differentiated THP1 cells and human monocyte-derived macrophages. Our data suggest that PLTP has anti-inflammatory capabilities in macrophages.