Rapid NMR Assignments of Proteins by Using Optimized Combinatorial Selective Unlabeling

A new approach for rapid resonance assignments in proteins based on amino acid selective unlabeling is presented. The method involves choosing a set of multiple amino acid types for selective unlabeling and identifying specific tripeptides surrounding the labeled residues from specific 2D NMR spectra in a combinatorial manner. The methodology directly yields sequence specific assignments, without requiring a contiguously stretch of amino acid residues to be linked, and is applicable to deuterated proteins. We show that a 2D N-15,H-1]HSQC spectrum with two 2D spectra can result in approximate to 50% assignments. The methodology was applied to two proteins: an intrinsically disordered protein (12kDa) and the 29kDa (268 residue) -subunit of Escherichia coli tryptophan synthase, which presents a challenging case with spectral overlaps and missing peaks. The method can augment existing approaches and will be useful for applications such as identifying active-site residues involved in ligand binding, phosphorylation, or protein-protein interactions, even prior to complete resonance assignments.

PolyMage: Automatic Optimization for Image Processing Pipelines

This paper presents the design and implementation of PolyMage, a domain-specific language and compiler for image processing pipelines. An image processing pipeline can be viewed as a graph of interconnected stages which process images successively. Each stage typically performs one of point-wise, stencil, reduction or data-dependent operations on image pixels. Individual stages in a pipeline typically exhibit abundant data parallelism that can be exploited with relative ease. However, the stages also require high memory bandwidth preventing effective utilization of parallelism available on modern architectures. For applications that demand high performance, the traditional options are to use optimized libraries like OpenCV or to optimize manually. While using libraries precludes optimization across library routines, manual optimization accounting for both parallelism and locality is very tedious. The focus of our system, PolyMage, is on automatically generating high-performance implementations of image processing pipelines expressed in a high-level declarative language. Our optimization approach primarily relies on the transformation and code generation capabilities of the polyhedral compiler framework. To the best of our knowledge, this is the first model-driven compiler for image processing pipelines that performs complex fusion, tiling, and storage optimization automatically. Experimental results on a modern multicore system show that the performance achieved by our automatic approach is up to 1.81x better than that achieved through manual tuning in Halide, a state-of-the-art language and compiler for image processing pipelines. For a camera raw image processing pipeline, our performance is comparable to that of a hand-tuned implementation.

Quaternary cocrystals: combinatorial synthetic strategies based on long-range synthon Aufbau modules (LSAM)

A synthetic strategy is outlined whereby a binary cocrystal may be developed in turn into a ternary and finally into a quaternary cocrystal. The strategy hinges on the concept of the long-range synthon Aufbau module (LSAM) which is a large supramolecular synthon containing more than one type of intermolecular interaction. Modulation of these interactions may be possible with the use of additional molecular components so that higher level cocrystals are produced. We report six quaternary cocrystals here. All are obtained as nearly exclusive crystallization products when four appropriate solid compounds are taken together in solution for crystallization.

Conversion of plastic waste into CNTs using Ni/Mo/MgO catalystAn optimization approach by mixture experiment

A combustion technique is used to study the synthesis of carbon nano tubes from waste plastic as a precursor and Ni/Mo/MgO as a catalyst. The catalytic activity of three components Ni, Mo, MgO is measured in terms of amount of carbon product obtained. Different proportions of metal ions are optimized using mixture experiment in Design expert software. D-optimal design technique is adopted due to nonsimplex region and presence of constraints in the mixture experiment. The activity of the components is observed to be interdependent and the component Ni is found to be more effective. The catalyst containing Ni0.8Mo0.1MgO0.1 yields more carbon product. The structure of catalyst and CNTs are studied by using SEM, XRD, and Raman spectroscopy. SEM analysis shows the formation of longer CNTs with average diameter of 40-50 nm.

Design Optimization of Thin-Film Transistors Based on a Metal-Substrate-Semiconductor Architecture for High DC Voltage Sensing

We discuss the potential application of high dc voltage sensing using thin-film transistors (TFTs) on flexible substrates. High voltage sensing has potential applications for power transmission instrumentation. For this, we consider a gate metal-substrate-semiconductor architecture for TFTs. In this architecture, the flexible substrate not only provides mechanical support but also plays the role of the gate dielectric of the TFT. Hence, the thickness of the substrate needs to be optimized for maximizing transconductance, minimizing mechanical stress, and minimizing gate leakage currents. We discuss this optimization, and develop n-type and p-type organic TFTs using polyvinyldene fluoride as the substrate-gate insulator. Circuits are also realized to achieve level shifting, amplification, and high drain voltage operation.

SMO: An Integrated Approach to Intra-array and Inter-array Storage Optimization

The polyhedral model provides an expressive intermediate representation that is convenient for the analysis and subsequent transformation of affine loop nests. Several heuristics exist for achieving complex program transformations in this model. However, there is also considerable scope to utilize this model to tackle the problem of automatic memory footprint optimization. In this paper, we present a new automatic storage optimization technique which can be used to achieve both intra-array as well as inter-array storage reuse with a pre-determined schedule for the computation. Our approach works by finding statement-wise storage partitioning hyper planes that partition a unified global array space so that values with overlapping live ranges are not mapped to the same partition. Our heuristic is driven by a fourfold objective function which not only minimizes the dimensionality and storage requirements of arrays required for each high-level statement, but also maximizes inter statement storage reuse. The storage mappings obtained using our heuristic can be asymptotically better than those obtained by any existing technique. We implement our technique and demonstrate its practical impact by evaluating its effectiveness on several benchmarks chosen from the domains of image processing, stencil computations, and high-performance computing.