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No abstract is provided for this article.

A number of large-scale distributed Internet applications could potentially benefit from some level of knowledge about the relative proximity between its participating host nodes. For example, the performance of large overlay networks could be improved if the application-level connectivity between the nodes in these networks is congruent with the underlying IP-level topology. Similarly, in the case of replicated Web content, client nodes could use topological information in selecting one of multiple available servers. For such applications, one need not find the optimal solution in order to achieve significant practical benefits. Thus, these applications, and presumably others like them, do not require exact topological information and can instead use sufficiently informative hints about the relative positions of Internet hosts. In this paper, we present a binning scheme whereby nodes partition themselves into bins such that nodes that fall within a given bin are relatively close to one another in terms of network latency. Our binning strategy is simple (requiring minimal support from any measurement infrastructure), scalable (requiring no form of global knowledge, each node only needs knowledge of a small number of well-known landmark nodes) and completely distributed (requiring no communication or cooperation between the nodes being binned). We apply this binning strategy to the two applications mentioned above: overlay network construction and server selection. We test our binning strategy and its application using simulation and Internet measurement traces. Our results indicate that the performance of these applications can be significantly improved by even the rather coarse-grained knowledge of topology offered by our binning scheme.

A significant bottleneck in synthetic biology involves screening large genetically encoded libraries for desirable phenotypes such as chemical production. However, transcription factor-based biosensors can be leveraged to screen thousands of genetic designs for optimal chemical production in engineered microbes. In this study we characterize two glutarate sensing transcription factors (CsiR and GcdR) from <i>Pseudomonas putida</i>. The genomic contexts of <i>csiR</i> homologues were analyzed, and their DNA binding sites were bioinformatically predicted. Both CsiR and GcdR were purified and shown to bind upstream of their coding sequencing <i>in vitro</i>. CsiR was shown to dissociate from DNA <i>in vitro</i> when exogenous glutarate was added, confirming that it acts as a genetic repressor. Both transcription factors and cognate promoters were then cloned into broad host range vectors to create two glutarate biosensors. Their respective sensing performance features were characterized, and more sensitive derivatives of the GcdR biosensor were created by manipulating the expression of the transcription factor. Sensor vectors were then reintroduced into <i>P. putida</i> and evaluated for their ability to respond to glutarate and various lysine metabolites. Additionally, we developed a novel mathematical approach to describe the usable range of detection for genetically encoded biosensors, which may be broadly useful in future efforts to better characterize biosensor performance.

Shark is a new data analysis system that marries query processing with complex analytics on large clusters. It leverages a novel distributed memory abstraction to provide a unified engine that can run SQL queries and sophisticated analytics functions (e.g. iterative machine learning) at scale, and efficiently recovers from failures mid-query. This allows Shark to run SQL queries up to 100X faster than Apache Hive, and machine learning programs more than 100X faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a MapReduce-like execution engine, and the fine-grained fault tolerance properties that such engine provides. It extends such an engine in several ways, including column-oriented in-memory storage and dynamic mid-query replanning, to effectively execute SQL. The result is a system that matches the speedups reported for MPP analytic databases over MapReduce, while offering fault tolerance properties and complex analytics capabilities that they lack.

Using calculations based on first principles, we demonstrate that the preferred interface in singles LiFePO₄ particles depends both on the particle size and morphology.

This report documents the program and the outcomes of Dagstuhl Seminar 13251 "Parallel Data Analysis" which was held in Schloss Dagstuhl - Leibniz Center for Informatics from June 16th 2013 to June 21st 2013. During the seminar, participants presented their current research and ongoing work, and open problems were discussed. The first part of this document describes seminar goals and topics, while the remainder gives an overview of the contents discussed during this event. Abstracts of a subset of the presentations given during the seminar are put together in this paper. Links to extended abstracts or full papers are provided, if available.

Phase transformations have been a prominent topic of study for both fundamental and applied science. Solid-liquid reaction–induced phase transformations can be hard to characterize, and the transformation mechanisms are often not fully understood. Here, we report reversible phase transformations between a metal (Pb) nanocrystal and a viscous liquid-like phase unveiled by in situ liquid cell transmission electron microscopy. The reversible phase transformations are obtained by modulating the electron current density (between 1000 and 3000 electrons Å −2 s −1 ). The metal-organic viscous liquid-like phase exhibits short-range ordering with a preferred Pb-Pb distance of 0.5 nm. Assisted by density functional theory and molecular dynamics calculations, we show that the viscous liquid-like phase results from the reactions of Pb with the CH 3 O fragments from the triethylene glycol solution under electron beam irradiation. Such reversible phase transformations may find broad implementations.

A series of novel C2-symmetric bisoxazoline ligands (benbox and benboxMe2) have been prepared. A variety of coordinatively unsaturated rhodium(III) complexes (3a−g, 4a−g) have been isolated. Trapping reactions of the agostically stabilized complex [RhCl2{(S,S)-ib-benbox(Me2)}] (4d) are described. Metal-to-aryl methyl migration reactions are described for the complex [RhMe{(S,S)-ip-benbox(Me2)}][BArf] (11c). Full characterization details and reactivity studies of air-stable, isolable, mononuclear, paramagnetic rhodium(II) complexes [RhCl2{(S,S)-tb-benbox(Me2)}(H)] (15e) and [RhCl2{dm-benbox(Me2)H}] (15g) are reported.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.