Gauging Correct Relative Rankings For Similarity Search

© 2015 ACM.One of the important tasks in link analysis is to quantify the similarity between two objects based on hyperlink structure. SimRank is an attractive similarity measure of this type. Existing work mainly focuses on absolute SimRank scores, and often harnesses an iterative paradigm to compute them. While these iterative scores converge to exact ones with the increasing number of iterations, it is still notoriously difficult to determine how well the relative orders of these iterative scores can be preserved for a given iteration. In this paper, we propose efficient ranking criteria that can secure correct relative orders of node-pairs with respect to SimRank scores when they are computed in an iterative fashion. Moreover, we show the superiority of our criteria in harvesting top-K SimRank scores and bucket orders from a full ranking list. Finally, viable empirical studies verify the usefulness of our techniques for SimRank top-K ranking and bucket ordering

Epitope mapping using mRNA display and a unidirectional nested deletion library

In vitro selection targeting an anti-polyhistidine monoclonal antibody was performed using mRNA display with a random, unconstrained 27-mer peptide library. After six rounds of selection, epitope-like peptides were identified that contain two to five consecutive, internal histidines and are biased for arginine residues, without any other identifiable consensus. The epitope was further refined by constructing a high-complexity, unidirectional fragment library from the final selection pool. Selection by mRNA display minimized the dominant peptide from the original selection to a 15-residue functional sequence (peptide Cmin: RHDAGDHHHHHGVRQ; K-D = 38 nM). Other peptides recovered from the fragment library selection revealed a separate consensus motif (ARRXA) C-terminal to the histidine track. Kinetics measurements made by surface plasmon resonance, using purified Fab (antigen-binding fragment) to prevent avidity effects, demonstrate that the selected peptides bind with 10- to 75-fold higher affinities than a hexahistidine peptide. The highest affinity peptides (K-D approximate to 10 nM) encode both a short histidine track and the ARRXA motif, suggesting that the motif and other flanking residues make important contacts adjacent to the core polyhistidine-binding site and can contribute > 2.5 kcal/mol of binding free energy. The fragment library construction methodology described here is applicable to the development of high-complexity protein or cDNA expression libraries for the identification of protein-protein interaction domains

Non-lethal PCR genotyping of single Drosophila

In Drosophila, genetic techniques relying on stochastic chromosomal rearrangements involve the generation and screening of a large number of fly stocks to isolate a few lines of interest. Here, we describe a PCR-based method allowing non-lethal molecular characterization of single flies. Using this procedure, individual candidate recombinant animals can be genotyped and selected one generation earlier than with extant methodology and, importantly, before stocks are established. This advance should significantly facilitate several of the most fundamental and routine techniques in Drosophila genetics

Toward improved identifiability of hydrologic model parameters: The information content of experimental data

We have developed a sequential optimization methodology, entitled the parameter identification method based on the localization of information (PIMLI) that increases information retrieval from the data by inferring the location and type of measurements that are most informative for the model parameters. The PIMLI approach merges the strengths of the generalized sensitivity analysis (GSA) method [Spear and Hornberger, 1980], the Bayesian recursive estimation (BARE) algorithm [Thiemann et al., 2001], and the Metropolis algorithm [Metropolis et al., 1953]. Three case studies with increasing complexity are used to illustrate the usefulness and applicability of the PIMLI methodology. The first two case studies consider the identification of soil hydraulic parameters using soil water retention data and a transient multistep outflow experiment (MSO), whereas the third study involves the calibration of a conceptual rainfall-runoff model

A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters

Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution of parameters in hydrologic models. However, MCMC methods require the a priori definition of a proposal or sampling distribution, which determines the explorative capabilities and efficiency of the sampler and therefore the statistical properties of the Markov Chain and its rate of convergence. In this paper we present an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), which is well suited to infer the posterior distribution of hydrologic model parameters. The SCEM-UA algorithm is a modified version of the original SCE-UA global optimization algorithm developed by Duan et al. [1992]. The SCEM-UA algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Three case studies demonstrate that the adaptive capability of the SCEM-UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis-Hastings samplers

A Peptide Core Motif for Binding to Heterotrimeric G Protein α Subunits

Recently, in vitro selection using mRNA display was used to identify a novel peptide sequence that binds with high affinity to G{alpha}i1. The peptide was minimized to a 9-residue sequence (R6A-1) that retains high affinity and specificity for the GDP-bound state of G{alpha}i1 and acts as a guanine nucleotide dissociation inhibitor (GDI). Here we demonstrate that the R6A-1 peptide interacts with G{alpha} subunits representing all four G protein classes, acting as a core motif for G{alpha} interaction. This contrasts with the consensus G protein regulatory(GPR) sequence, a 28-mer peptide GDI derived from the GoLoco (G{alpha}i/0-Loco interaction)/GPR motif that shares no homology with R6A-1 and binds only to G{alpha}i1-3 in this assay. Binding of R6A-1 is generally specific to the GDP-bound state of the G{alpha} subunits and excludes association with G{beta}{gamma}. R6A-G{alpha}i1 complexes are resistant to trypsin digestion and exhibit distinct stability in the presence of Mg2+, suggesting that the R6A and GPR peptides exert their activities using different mechanisms. Studies using G{alpha}i1/G{alpha}s chimeras identify two regions of G{alpha}i1 (residues 1–35 and 57–88) as determinants for strong R6A-Gi{alpha}1 interaction. Residues flanking the R6A-1 peptide confer unique binding properties, indicating that the core motif could be used as a starting point for the development of peptides exhibiting novel activities and/or specificity for particular G protein subclasses or nucleotide-bound states

Effective and efficient algorithm for multiobjective optimization of hydrologic models

Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity

Quantum particle on hyperboloid

We present quantization of particle dynamics on one-sheet hyperboloid embedded in three dimensional Minkowski space. Taking account of all global symmetries enables unique quantization. Making use of topology of canonical variables not only simplifies calculations but also gives proper framework for analysis.Comment: 7 pages, no figures, revtex