Summary CD4+ CD25+ Foxp3+ naturally occurring regulatory T cells (nTreg) are potent inhibitors of almost all immune responses. However, it is unclear how this minor population of cells is capable of exerting its powerful suppressor effects. To determine whether nTreg mediate part of their suppressor function by rendering naive T cells anergic or by converting them to the suppressor phenotype, we cocultured mouse nTreg with naive CD4+ CD25– T cells from T‐cell receptor (TCR) transgenic mice on a RAG deficient (RAG–/–) background in the presence of anti‐CD3 and interleukin‐4 (IL‐4) to promote cell viability. Two distinct responder cell populations could be recovered from the cocultures. One population remained undivided in the coculture and was non‐responsive to restimulation with anti‐CD3 or exogenous IL‐2, and could not up‐regulate IL‐2 mRNA or CD25 expression upon TCR restimulation. Those responder cells that had divided in the coculture were anergic to restimulation with anti‐CD3 but responded to restimulation with IL‐2. The undivided population was capable of suppressing the response of fresh CD4+ CD25– T cells and CD8+ T cells, while the divided population was only marginally suppressive. Although cell contact between the induced regulatory T cell (iTreg) and the responders was required for suppression to be observed, anti‐transforming growth factor‐β partially abrogated their suppressive function. The iTreg did not express Foxp3. Therefore nTreg are not only able to suppress immune responses by inhibiting cytokine production by CD4+ CD25– responder cells, but also appear to modulate the responder cells to render them both anergic and suppressive.

It is quite common for networks emerging nowadays to have labels or textual contents on the nodes. On such networks, we study the problem of top-k nearest keyword (k-NK) search. In a network G modeled as an undirected graph, each node is attached with zero or more keywords, and each edge is assigned with a weight measuring its length. Given a query node q in G and a keyword λ, a k-NK query seeks k nodes which contain λ and are nearest to q. k-NK is not only useful as a stand-alone query but also as a building block for tackling complex graph pattern matching problems.The key to an accurate k-NK result is a precise shortest distance estimation in a graph. Based on the latest distance oracle technique, we build a shortest path tree for a distance oracle and use the tree distance as a more accurate estimation. With such representation, the original k-NK query on a graph can be reduced to answering the query on a set of trees and then assembling the results obtained from the trees. We propose two efficient algorithms to report the exact k-NK result on a tree. One is query time optimized for a scenario when a small number of result nodes are of interest to users. The other handles k-NK queries for an arbitrarily large k efficiently. In obtaining a k-NK result on a graph from that on trees, a global storage technique is proposed to further reduce the index size and the query time. Extensive experimental results conform with our theoretical findings, and demonstrate the effectiveness and efficiency of our k-NK algorithms on large real graphs.

Shortest distance query between two nodes is a fundamental operation in large-scale networks. Most existing methods in the literature take a landmark embedding approach, which selects a set of graph nodes as landmarks and computes the shortest distances from each landmark to all nodes as an embedding. To handle a shortest distance query between two nodes, the precomputed distances from the landmarks to the query nodes are used to compute an approximate shortest distance based on the triangle inequality.In this paper, we analyze the factors that affect the accuracy of the distance estimation in the landmark embedding approach. In particular we find that a globally selected, query-independent landmark set plus the triangulation based distance estimation introduces a large relative error, especially for nearby query nodes. To address this issue, we propose a query-dependent local landmark scheme, which identifies a local landmark close to the specific query nodes and provides a more accurate distance estimation than the traditional global landmark approach. Specifically, a local landmark is defined as the least common ancestor of the two query nodes in the shortest path tree rooted at a global landmark. We propose efficient local landmark indexing and retrieval techniques, which are crucial to achieve low offline indexing complexity and online query complexity. Two optimization techniques on graph compression and graph online search are also proposed, with the goal to further reduce index size and improve query accuracy. Our experimental results on large-scale social networks and road networks demonstrate that the local landmark scheme reduces the shortest distance estimation error significantly when compared with global landmark embedding.

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