Search

The envelope of Human Immunodeficiency Virus type 1 (HIV-1) consists of a liquid-ordered membrane enriched in raft lipids and containing the viral glycoproteins. Previous studies demonstrated that changes in viral membrane lipid composition affecting membrane structure or curvature can impair infectivity. Here, we describe novel antiviral compounds that were identified by screening compound libraries based on raft lipid-like scaffolds. Three distinct molecular structures were chosen for mode-of-action studies, a sterol derivative (J391B), a sphingosine derivative (J582C) and a long aliphatic chain derivative (IBS70). All three target the viral membrane and inhibit virus infectivity at the stage of fusion without perturbing virus stability or affecting virion-associated envelope glycoproteins. Their effect did not depend on the expressed envelope glycoproteins or a specific entry route, being equally strong in HIV pseudotypes carrying VSV-G or MLV-Env glycoproteins. Labeling with laurdan, a reporter of membrane order, revealed different membrane structure alterations upon compound treatment of HIV-1, which correlated with loss of infectivity. J582C and IBS70 decreased membrane order in distinctive ways, whereas J391B increased membrane order. The compounds' effects on membrane order were reproduced in liposomes generated from extracted HIV lipids and thus independent both of virion proteins and of membrane leaflet asymmetry. Remarkably, increase of membrane order by J391B required phosphatidylserine, a lipid enriched in the HIV envelope. Counterintuitively, mixtures of two compounds with opposite effects on membrane order, J582C and J391B, did not neutralize each other but synergistically inhibited HIV infection. Thus, altering membrane order, which can occur by different mechanisms, constitutes a novel antiviral mode of action that may be of general relevance for enveloped viruses and difficult to overcome by resistance development.

In this paper, we present BlinkDB, a massively parallel, approximate query engine for running interactive SQL queries on large volumes of data. BlinkDB allows users to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars. To achieve this, BlinkDB uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratified samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query's accuracy or response time requirements. We evaluate BlinkDB against the well-known TPC-H benchmarks and a real-world analytic workload derived from Conviva Inc., a company that manages video distribution over the Internet. Our experiments on a 100 node cluster show that BlinkDB can answer queries on up to 17 TBs of data in less than 2 seconds (over 200 x faster than Hive), within an error of 2-10%., National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158), United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331))

Modern data analytics applications typically process massive amounts of data on clusters of tens, hundreds, or thousands of machines to support near-real-time decisions.The quantity of data and limitations of disk and memory bandwidth often make it infeasible to deliver answers at interactive speeds. However, it has been widely observed that many applications can tolerate some degree of inaccuracy. This is especially true for exploratory queries on data, where users are satisfied with "close-enough" answers if they can come quickly. A popular technique for speeding up queries at the cost of accuracy is to execute each query on a sample of data, rather than the whole dataset. To ensure that the returned result is not too inaccurate, past work on approximate query processing has used statistical techniques to estimate "error bars" on returned results. However, existing work in the sampling-based approximate query processing (S-AQP) community has not validated whether these techniques actually generate accurate error bars for real query workloads. In fact, we find that error bar estimation often fails on real world production workloads. Fortunately, it is possible to quickly and accurately diagnose the failure of error estimation for a query. In this paper, we show that it is possible to implement a query approximation pipeline that produces approximate answers and reliable error bars at interactive speeds., National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158), Lawrence Berkeley National Laboratory (Award 7076018), United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331), Amazon.com (Firm), Google (Firm), SAP Corporation, Thomas and Stacey Siebel Foundation, Apple Computer, Inc., Cisco Systems, Inc., Cloudera, Inc., EMC Corporation, Ericsson, Inc., Facebook (Firm)

In this demonstration, we present BlinkDB, a massively parallel, sampling-based approximate query processing framework for running interactive queries on large volumes of data. The key observation in BlinkDB is that one can make reasonable decisions in the absence of perfect answers. BlinkDB extends the Hive/HDFS stack and can handle the same set of SPJA (selection, projection, join and aggregate) queries as supported by these systems. BlinkDB provides real-time answers along with statistical error guarantees, and can scale to petabytes of data and thousands of machines in a fault-tolerant manner. Our experiments using the TPC-H benchmark and on an anonymized real-world video content distribution workload from Conviva Inc. show that BlinkDB can execute a wide range of queries up to 150x faster than Hive on MapReduce and 10--150x faster than Shark (Hive on Spark) over tens of terabytes of data stored across 100 machines, all with an error of 2--10%., National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158), QUALCOMM Inc., Amazon.com (Firm), Google (Firm), SAP Corporation, Blue Goji, Cisco Systems, Inc., Cloudera, Inc., Ericsson, Inc., General Electric Company, Hewlett-Packard Company, Intel Corporation, MarkLogic Corporation, Microsoft Corporation, NetApp, Oracle Corporation, Splunk Inc., VMware, Inc., United States. Defense Advanced Research Projects Agency (Contract FA8650-11-C-7136)

Polycyclic nitrogen containing heterocycles form the basic skeleton of numerous alkaloids and physiologically active drugs. Alloyohimbane was obtained from 3,4-dihydro-â-carboline using an iron-mediated [2+2+1] cycloaddition as the key-step. The bis-TMS-diyne was conveniently obtained by the C-alkylation of 3,4-dihydro-â-carboline followed by N-alkylation. Demetalation of the iron-complex followed by hydrogenation, E-ring expansion, and reduction provided alloyohimbane, a structurally and biologically interesting substance, via a linear eight-step sequence in 7% overall yield based on 3,4-dihydro-â-carboline. Another sequence provided (±)-alloyohimbane and (±)-3-epi-alloyohimbane in nine steps. The pyrrole unit occurs in a variety of naturally occurring compounds, pharmaceutical products and polymers. A novel two-step procedure for the synthesis of pyrroles by addition of a propargyl Grignard reagent to a Schiff base and subsequent silver(I)-promoted oxidative cyclization of the resulting homopropargylamine has been developed. The generality of this reaction was proven by the synthesis of a broad variety of substituted pyrroles using silver(I)-promoted cyclization. A three-step synthesis of (±)-harmicine, a natural product isolated from the Malaysian plant Kopsia griffithii having strong anti-leishmania activity, from 3,4-dihydro-â-carboline is achieved by addition of 3-trimethylsilylpropargyl Grignard reagent, Ag(I)-promoted oxidative cyclization to a pyrrole, and chemoselective hydrogenation of pyrrole ring. Total synthesis of anti-tumor active crispine A and biologically active 1,2,3,5,6,10b-hexahydropyrrolo[2,1-a]isoquinoline have been achieved in three steps using silver(I)-promoted oxidative cyclization as key step.

We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a two-step algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms.