SUMMARY Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis. The task is handled by iterative algorithms, whose performance is compromised by the compounded limitations of image alignment and K-means clustering. Here we describe an approach, iterative stable alignment and clustering (ISAC) that, relying on a new clustering method and on the concepts of stability and reproducibility, can extract validated, homogeneous subsets of images. ISAC requires only a small number of simple parameters and, with minimal human intervention, can eliminate bias from two-dimensional image clustering and maximize the quality of group averages that can be used for ab initio three-dimensional structural determination and analysis of macromolecular conformational variability. Repeated testing of the stability and reproducibility of a solution within ISAC eliminates heterogeneous or incorrect classes and introduces critical validation to the process of EM image clustering.

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In single particle analysis, two-dimensional (2-D) alignment is a fundamental step intended to put into register various particle projections of biological macromolecules collected at the electron microscope. The efficiency and quality of three-dimensional (3-D) structure reconstruction largely depends on the computational speed and alignment accuracy of this crucial step. In order to improve the performance of alignment, we introduce a new method that takes advantage of the highly accurate interpolation scheme based on the gridding method, a version of the nonuniform Fast Fourier Transform, and utilizes a multi-dimensional optimization algorithm for the refinement of the orientation parameters. Using simulated data, we demonstrate that by using less than half of the sample points and taking twice the runtime, our new 2-D alignment method achieves dramatically better alignment accuracy than that based on quadratic interpolation. We also apply our method to image to volume registration, the key step in the single particle EM structure refinement protocol. We find that in this case the accuracy of the method not only surpasses the accuracy of the commonly used real-space implementation, but results are achieved in much shorter time, making griddingbased alignment a perfect candidate for efficient structure determination in single particle analysis.

This research investigated the use of wood ash to partially replace cement or sand in conventional concrete, roller compacted concrete (RCC), and flowable fill. The main focus was to determine how the wood ash addition affected the main fresh and hardened properties of these materials. It was found that the wood ash could be successfully incorporated into the conventional concrete. In particular, the wood ash addition not only accelerated the setting, but also improved the early and the 28-day compressive strength of concrete that contained the blast furnace slag. It was also observed that the wood ash could be positively added into RCC to facilitate the compaction and reduce the risk of segregation. In addition, the wood ash can be beneficially introduced into the flowable fill mixtures to facilitate flow, to alleviate bleeding and subsidence, as well as to achieve controlled strength especially when combined with the class C or the class F fly ash.

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