Conditional Reliability in Uncertain Graphs

Network reliability is a well-studied problem that requires to measure the probability that a target node is reachable from a source node in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Many approaches and problem variants have been considered in the literature, all assuming that edge-existence probabilities are fixed. Nevertheless, in real-world graphs, edge probabilities typically depend on external conditions. In metabolic networks a protein can be converted into another protein with some probability depending on the presence of certain enzymes. In social influence networks the probability that a tweet of some user will be re-tweeted by her followers depends on whether the tweet contains specific hashtags. In transportation networks the probability that a network segment will work properly or not might depend on external conditions such as weather or time of the day. In this paper we overcome this limitation and focus on conditional reliability, that is assessing reliability when edge-existence probabilities depend on a set of conditions. In particular, we study the problem of determining the k conditions that maximize the reliability between two nodes. We deeply characterize our problem and show that, even employing polynomial-time reliability-estimation methods, it is NP-hard, does not admit any PTAS, and the underlying objective function is non-submodular. We then devise a practical method that targets both accuracy and efficiency. We also study natural generalizations of the problem with multiple source and target nodes. An extensive empirical evaluation on several large, real-life graphs demonstrates effectiveness and scalability of the proposed methods.Comment: 14 pages, 13 figure

A Robust Bootstrap Test for Mediation Analysis

Mediation analysis is central to theory building and testing in organizations research. Management scholars often use linear regression analysis based on normal-theory maximum likelihood estimators to test mediation. However, these estimators are very sensitive to deviations from normality assumptions, such as outliers or heavy tails of the observed distribution. This sensitivity seriously threatens the empirical testing of theory about mediation mechanisms, as many empirical studies lack reporting of outlier treatments and checks on model assumptions. To overcome this threat, we develop a fast and robust mediation method that yields reliable results even when the data deviate from normality assumptions. Simulation studies show that our method is both superior in estimating the effect size and more reliable in assessing its significance than the existing methods. We illustrate the mechanics of our proposed method in three empirical cases and provide freely available software in R and SPSS to enhance its accessibility and adoption by researchers and practitioners

Effective cutaneous vaccination using an inactivated chikungunya virus vaccine delivered by Foroderm

Foroderm is a new cutaneous delivery technology that uses high-aspect ratio, cylindrical silica microparticles, that are massaged into the skin using a 3D-printed microtextured applicator, in order to deliver payloads across the epidermis. Herein we show that this technology is effective for delivery of a non-adjuvanted, inactivated, whole-virus chikungunya virus vaccine in mice, with minimal post-vaccination skin reactions. A single topical Foroderm-based vaccination induced T cell, Th1 cytokine and antibody responses, which provided complete protection against viraemia and disease after challenge with chikungunya virus. Foroderm vaccination was shown to deliver fluorescent, virus-sized beads across the epidermis, with beads subsequently detected in draining lymph nodes. Foroderm vaccination also stimulated the egress of MHC II+ antigen presenting cells from the skin. Foroderm thus has potential as a simple, cheap, effective, generic, needle-free technology for topical delivery of vaccines

Wet mount and histology of pipefish gills.

<p>a: Wet mount in sterile sea water with numerous protruding epitheliocystis lesions clearly visible (open arrowheads). b: Gill lamella with focal intracellular cyst in epithelial cell, 40 µm long axis, with dark basophilic granular material (black arrowhead), the epithelium of affected lamella shows no pathological changes and no inflammatory reaction, scale bar = 10 µm. c: multiple <i>Trichodina</i> sp. between lamellae (arrows), no associated pathological changes, scale bar = 10 µm.</p

Wet mount and FISH of isolated inclusions.

<p>a-c: Wet mount of freshly isolated cyst in sterile sea water (a), punctured with a glass microinjection needle (b), releasing a thick cloud of bacteria (* c). FISH of isolated cyst, labelled with a eubacterial probe (green, d) and a <i>Chlamydiales</i>-specific probe (red, e), with the combined signals (f). The FISH images were collected as 3D image stacks by CLSM, deconvolved and the resulting image was compressed into a single 2D-image, shown here. Scale bars = 50 µm.</p

Transmission electron microscopy of epitheliocystis lesions showing features typical for <i>Candidatus</i> Syngnamydia venezia.

<p>Both a and b give an overview of the dense cell packing. In c and d, endosymbionts showing one or two electron-lucent regions (<), possibly representative of dividing bacteria, and maximally 3.4 µm (d, *) in length. The rippled outer membrane (e) as well as the angular forms (f) are typical characteristics. Scale bars = 1 µm.</p

Phylogenetic tree of members of the phylum <i>Chlamydiae</i>, including epitheliocystis agents, calculated using NJ and MP analysis of the full length 16S rRNA gene sequence (1477 bp), using <i>Ricksettia</i> sp. as an outgroup.

<p>The percentage of replicate trees in which the associated taxa cluster together in the bootstrap test (1000 replicates) is indicated. Edited sequences were aligned in ClustalW2. Bootstrap values of 85%, and 43% in the NJ and MP trees, respectively, separated the new species from its closest relatives in the <i>Simkaniaceae</i> family.</p