Enthalpy/entropy compensation: Influence of DNA flanking sequence on the binding of 7-amino actinomycin D to its primary binding site in short DNA duplexes

The effect of the context of the flanking sequence on ligand binding to DNA oligonucleotides that contain consensus binding sites was investigated for the binding of the intercalator 7-amino actinomycin D. Seven self-complementary DNA oligomers each containing a centrally located primary binding site, 5'-A-G-C-T-3', flanked on either side by the sequences (AT)(n) or (AA)(n) (with n = 2, 3, 4) and AA(AT)(2), were studied. For different flanking sequences, (AA)(n)-series or (AT)(n)-series, differential fluorescence enhancements of the ligand due to binding were observed. Thermodynamic studies indicated that the flanking sequences not only affected DNA stability and secondary structure but also modulated ligand binding to the primary binding site. The magnitude of the ligand binding affinity to the primary site was inversely related to the sequence dependent stability. The enthalpy of ligand binding was directly measured by isothermal titration calorimetry, and this made it possible to parse the binding free energy into its energetic and entropic terms.

Applications of generalized torsion angles in studies on quantitative structure-activity relationship

In this article, generalized torsion angles of derivatives of 1-[(2-hydroxyethoxy)methy1]-6(phenylthio)thymine(HEPT) were calculated, which include abundant three dimensional information of molecules. Molecular similarity matrix was built based on the calculated generalized torsion angles. These similarities were taken as the new variables, and the new variables were selected by using Leaps-and-Bounds regression analysis. Multiple regression analysis and neural networks were performed, and the satisfactory results were achieved by using the neural networks.

A Connection Between GRBF and MLP

Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approximation properties, theoretically and experimentally. Are they related? The main point of this paper is to show that for normalized inputs, multilayer perceptron networks are radial function networks (albeit with a non-standard radial function). This provides an interpretation of the weights w as centers t of the radial function network, and therefore as equivalent to templates. This insight may be useful for practical applications, including better initialization procedures for MLP. In the remainder of the paper, we discuss the relation between the radial functions that correspond to the sigmoid for normalized inputs and well-behaved radial basis functions, such as the Gaussian. In particular, we observe that the radial function associated with the sigmoid is an activation function that is good approximation to Gaussian basis functions for a range of values of the bias parameter. The implication is that a MLP network can always simulate a Gaussian GRBF network (with the same number of units but less parameters); the converse is true only for certain values of the bias parameter. Numerical experiments indicate that this constraint is not always satisfied in practice by MLP networks trained with backpropagation. Multiscale GRBF networks, on the other hand, can approximate MLP networks with a similar number of parameters.

Generalized Low-Rank Approximations

We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving {\\em weighted} low rank approximation problems, which, unlike simple matrix factorization problems, do not admit a closed form solution in general. We analyze, in addition, the nature of locally optimal solutions that arise in this context, demonstrate the utility of accommodating the weights in reconstructing the underlying low rank representation, and extend the formulation to non-Gaussian noise models such as classification (collaborative filtering).

Computational Structure of GPSG Models: Revised Generalized Phrase Structure Grammar

The primary goal of this report is to demonstrate how considerations from computational complexity theory can inform grammatical theorizing. To this end, generalized phrase structure grammar (GPSG) linguistic theory is revised so that its power more closely matches the limited ability of an ideal speaker--hearer: GPSG Recognition is EXP-POLY time hard, while Revised GPSG Recognition is NP-complete. A second goal is to provide a theoretical framework within which to better understand the wide range of existing GPSG models, embodied in formal definitions as well as in implemented computer programs. A grammar for English and an informal explanation of the GPSG/RGPSG syntactic features are included in appendices.

Volumetric consequences of particle loss by grading entropy.

Chemical and biological processes, such as dissolution in gypsiferous sands and biodegradation in waste refuse, result in mass or particle loss, which in turn lead to changes in solid and void phase volumes and grading. Data on phase volume and grading changes have been obtained from oedometric dissolution tests on sand–salt mixtures. Phase volume changes are defined by a (dissolution-induced) void volume change parameter (Λ). Grading changes are interpreted using grading entropy coordinates, which allow a grading curve to be depicted as a single data point and changes in grading as a vector quantity rather than a family of distribution curves. By combining Λ contours with pre- to post-dissolution grading entropy coordinate paths, an innovative interpretation of the volumetric consequences of particle loss is obtained. Paths associated with small soluble particles, the loss of which triggers relatively little settlement but large increase in void ratio, track parallel to the Λ contours. Paths associated with the loss of larger particles, which can destabilise the sand skeleton, tend to track across the Λ contours.

Transformation Based Interpolation with Generalized Representative Values

Z. Huang and Q. Shen. Transformation Based Interpolation with Generalized Representative Values. Proceedings of the 14th International Conference on Fuzzy Systems, pages 821-826.

Fuzzy interpolation with generalized representative values

Z. Huang and Q. Shen. Fuzzy interpolation with generalized representative values. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 161-171.

Generalized semi-geostrophic theory on a sphere

Douglas, Robert; Cullen, M.J.P.; Roulston, I.; Sewell, M.J., (2005) 'Generalized semi-geostrophic theory on a sphere', Journal of Fluid Mechanics 531 pp.123-157 RAE2008

itmBench: Generalized API for Internet Traffic Managers

Internet Traffic Managers (ITMs) are special machines placed at strategic places in the Internet. itmBench is an interface that allows users (e.g. network managers, service providers, or experimental researchers) to register different traffic control functionalities to run on one ITM or an overlay of ITMs. Thus itmBench offers a tool that is extensible and powerful yet easy to maintain. ITM traffic control applications could be developed either using a kernel API so they run in kernel space, or using a user-space API so they run in user space. We demonstrate the flexibility of itmBench by showing the implementation of both a kernel module that provides a differentiated network service, and a user-space module that provides an overlay routing service. Our itmBench Linux-based prototype is free software and can be obtained from http://www.cs.bu.edu/groups/itm/.

Entropy Loss is Maximal for Uniform Inputs

A secure sketch (defined by Dodis et al.) is an algorithm that on an input w produces an output s such that w can be reconstructed given its noisy version w' and s. Security is defined in terms of two parameters m and m˜ : if w comes from a distribution of entropy m, then a secure sketch guarantees that the distribution of w conditioned on s has entropy m˜ , where λ = m−m˜ is called the entropy loss. In this note we show that the entropy loss of any secure sketch (or, more generally, any randomized algorithm) on any distribution is no more than it is on the uniform distribution.

On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines.