As scientific instruments and computer simulations produce more and more data, the task of locating the essential information to gain insight becomes increasingly difficult. FastBit is an efficient software tool to address this challenge. In this article, we present a summary of the key techniques, namely bitmap compression, encoding and binning. The advances in these techniques have led to a search tool that can answer structured (SQL) queries orders of magnitude faster than popular database systems. To illustrate how FastBit is used in applications, we present three examples involving a high-energy physics experiment, a combustion simulation, and an accelerator simulation. In each case, FastBit significantly reduces the response time and enables interactive exploration on terabytes of data.

Performing drug screening of tissue derived from cancer patient biopsies using physiologically relevant 3D tumour models presents challenges due to the limited amount of available cell material. Here, we present a microfluidic platform that enables drug screening of cancer cell-enriched multicellular spheroids derived from tumour biopsies, allowing extensive anticancer compound screening prior to treatment. This technology was validated using cell lines and then used to screen primary human prostate cancer cells, grown in 3D as a heterogeneous culture from biopsy-derived tissue. The technology enabled the formation of repeatable drug concentration gradients across an array of spheroids without external fluid actuation, delivering simultaneously a range of drug concentrations to multiple sized spheroids, as well as replicates for each concentration. As proof-of-concept screening, spheroids were generated from two patient biopsies and a panel of standard-of-care compounds for prostate cancer were tested. Brightfield and fluorescence images were analysed to provide readouts of spheroid growth and health, as well as drug efficacy over time. Overall, this technology could prove a useful tool for personalised medicine and future drug development, with the potential to provide cost- and time-reduction in the healthcare delivery.

Understanding the earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array of distributed supercomputers. The resulting data archive, spread over several sites, currently contains upwards of 100 TB of simulation data and is growing rapidly. Looking toward mid-decade and beyond, we must anticipate and prepare for distributed climate research data holdings of many petabytes. The Earth System Grid (ESG) is a collaborative interdisciplinary project aimed at addressing the challenge of enabling management, discovery, access, and analysis of these critically important datasets in a distributed and heterogeneous computational environment.The problem is fundamentally a Grid problem. Building upon the Globus toolkit and a variety of other technologies, ESG is developing an environment that addresses authentication, authorization ).I. Foster and V. Nefedova are with the Argonne National

Abstract-Many scientific applications need support from a communication infrastructure that provides predictable performance, which requires effective algorithms for bandwidth reservations. Network reservation systems such as ESnet's OSCARS, establish guaranteed bandwidth of secure virtual circuits for a certain bandwidth and length of time. However, users currently cannot inquire about bandwidth availability, nor have alternative suggestions when reservation requests fail. In general, the number of reservation options is exponential with the number of nodes n, and current reservation commitments. We present a novel approach for path finding in time-dependent networks taking advantage of user-provided parameters of total volume and time constraints, which produces options for earliest completion and shortest duration. The theoretical complexity is only O(n 2 r 2 ) in the worst-case, where r is the number of reservations in the desired time interval. We have implemented our algorithm and developed efficient methodologies for incorporation into network reservation frameworks. Performance measurements confirm the theoretical predictions.

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