We report an approach for determining the structure of macromolecular assemblies by the combined application of cryo-electron microscopy (cryo-EM) and site-directed spin labeling electron paramagnetic resonance spectroscopy (EPR). This approach is illustrated for Hsp16.5, a small heat shock protein that prevents the aggregation of nonnative proteins. The structure of Hsp16.5 has been previously studied by both cryo-EM and X-ray crystallography. The crystal structure revealed a roughly spherical protein shell with dodecameric symmetry; however, residues 1-32 were found to be disordered. The cryo-EM reconstruction at 13 A resolution appeared similar to the crystal structure but with additional internal density corresponding to the N-terminal regions of the 24 subunits. In this study, a systematic application of site-directed spin labeling and EPR spectroscopy was carried out. By combining the EPR constraints from spin label accessibilities and proximities with the cryo-EM density, we obtained an atomic model for a portion of the Hsp16.5 N-terminal region in the context of the oligomeric complex.

Abstract. Smart disks, a type of processor-embedded active I/O devices, with their on-disk memory and network interface controller, can be viewed as processing elements with attached storage. The growing size and access patterns of today's large I/O-intensive applications require architectures whose processing power scales with the storage capacity. We evaluate a distributed smart disk architecture with representative I/O-intensive workloads including TPC-H queries, association rule mining, data clustering, and 2-D fast Fourier transform applications to study the proposed architecture.

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Built upon new data organization and access characteristics, MEMS-based storage devices have come under consideration as an alternative to disks for large data-intensive applications. While not already in commercial production, MEMS-based storage devices have outperformed disks in device-level simulations. Processor-embedded distributed disks improved performance of workloads by offloading application-level processing to the storage. To exploit the potential benefits offered by these emerging storage technologies and offloading models, we propose a processorembedded distributed MEMS-based storage architecture. Using validated MEMS device models, we evaluate the proposed architecture with representative database and data mining workloads. Our results show that MEMS-based storage improves the overall performance of these workloads over disk-based systems. Furthermore, MEMS-based storage devices transformed the characteristics of several workloads, indicating a shift of performance bottleneck from I/O to the interconnect or processing power of the storage system, which can impact the design points for future storage architectures.

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In computer vision and pattern recognition applications, there are usually a vast number of unlabelled data whereas the labelled data are very limited. Active learning is a kind of method that selects the most representative or informative examples for labelling and training; thus, the best prediction accuracy can be achieved. A novel active learning algorithm is proposed here based on one‐versus‐one strategy support vector machine (SVM) to solve multi‐class image classification. A new uncertainty measure is proposed based on some binary SVM classifiers and some of the most uncertain examples are selected from SVM output. To ensure that the selected examples are diverse from each other, Gaussian kernel is adopted to measure the similarity between any two examples. From the previous selected examples, a batch of diverse and uncertain examples are selected by the dynamic programming method for labelling. The experimental results on two datasets demonstrate the effectiveness of the proposed algorithm.

In this study, three single-chamber microbial fuel cells (MFCs), each having Pt-coated carbon cloth as a cathode and four bamboo charcoal (BC) plates as an anode, were run in a fed-batch mode, individually and in series. Simulated potato-processing wastewater was used as a substrate for supporting the growth of a mixed bacterial culture. The maximum power output increased from 0.386 mW with one MFC to 1.047 mW with three MFCs connected in series. The maximum power density, however, decreased from 576 mW/m2 (normalized to the cathode area) with one MFC to 520 mW/m2 with three MFCs in series. The experimental results showed that power can be increased by connecting the MFCs in series; however, choosing low resistance BC is crucial for increasing power density.