Functional and biophysical analysis of the C-terminus of the CGRP-receptor; a family B GPCR

G-protein coupled receptors (GPCRs) typically have a functionally important C-terminus which, in the largest subfamily (family A), includes a membrane-parallel eighth helix. Mutations of this region are associated with several diseases. There are few C-terminal studies on the family B GPCRs and no data supporting the existence of a similar eighth helix in this second major subfamily, which has little or no sequence homology to family A GPCRs. Here we show that the C-terminus of a family B GPCR (CLR) has a disparate region from N400 to C436 required for CGRP-mediated internalization, and a proximal region of twelve residues (from G388 to W399), in a similar position to the family A eighth helix, required for receptor localization at the cell surface. A combination of circular and linear dichroism, fluorescence and modified waterLOGSY NMR spectroscopy (SALMON) demonstrated that a peptide mimetic of this domain readily forms a membrane-parallel helix anchored to the liposome by an interfacial tryptophan residue. The study reveals two key functions held within the C-terminus of a family B GPCR and presents support for an eighth helical region with striking topological similarity to the nonhomologous family A receptor. This helix structure appears to be found in most other family B GPCRs.

Sparse signal representation by adaptive non-uniform B-spline dictionaries on a compact interval

Non-uniform B-spline dictionaries on a compact interval are discussed in the context of sparse signal representation. For each given partition, dictionaries of B-spline functions for the corresponding spline space are built up by dividing the partition into subpartitions and joining together the bases for the concomitant subspaces. The resulting slightly redundant dictionaries are composed of B-spline functions of broader support than those corresponding to the B-spline basis for the identical space. Such dictionaries are meant to assist in the construction of adaptive sparse signal representation through a combination of stepwise optimal greedy techniques.

Research cultures and the pragmatic functions of humor in academic research presentations:A corpus-assisted analysis

Based on a corpus of English, German, and Polish spoken academic discourse, this article analyzes the distribution and function of humor in academic research presentations. The corpus is the result of a European research cooperation project consisting of 300,000 tokens of spoken academic language, focusing on the genres research presentation, student presentation, and oral examination. The article investigates difference between the German and English research cultures as expressed in the genre of specialist research presentations, and the role of humor as a pragmatic device in their respective contexts. The data is analyzed according to the paradigms of corpus-assisted discourse studies (CADS). The findings show that humor is used in research presentations as an expression of discourse reflexivity. They also reveal a considerable difference in the quantitative distribution of humor in research presentations depending on the educational, linguistic, and cultural background of the presenters, thus confirming the notion of different research cultures. Such research cultures nurture distinct attitudes to genres of academic language: whereas in one of the cultures identified researchers conform with the constraints and structures of the genre, those working in another attempt to subvert them, for example by the application of humor. © 2012 Elsevier B.V.

Paradoxical psychometric functions ("swan functions") are explained by dilution masking in four stimulus dimensions

The visual system dissects the retinal image into millions of local analyses along numerous visual dimensions. However, our perceptions of the world are not fragmentary, so further processes must be involved in stitching it all back together. Simply summing up the responses would not work because this would convey an increase in image contrast with an increase in the number of mechanisms stimulated. Here, we consider a generic model of signal combination and counter-suppression designed to address this problem. The model is derived and tested for simple stimulus pairings (e.g. A + B), but is readily extended over multiple analysers. The model can account for nonlinear contrast transduction, dilution masking, and signal combination at threshold and above. It also predicts nonmonotonic psychometric functions where sensitivity to signal A in the presence of pedestal B first declines with increasing signal strength (paradoxically dropping below 50% correct in two-interval forced choice), but then rises back up again, producing a contour that follows the wings and neck of a swan. We looked for and found these "swan" functions in four different stimulus dimensions (ocularity, space, orientation, and time), providing some support for our proposal.

Fast reconstruction of harmonic functions from Cauchy data using the Dirichlet-to-Neumann map and integral equations

We propose and investigate a method for the stable determination of a harmonic function from knowledge of its value and its normal derivative on a part of the boundary of the (bounded) solution domain (Cauchy problem). We reformulate the Cauchy problem as an operator equation on the boundary using the Dirichlet-to-Neumann map. To discretize the obtained operator, we modify and employ a method denoted as Classic II given in [J. Helsing, Faster convergence and higher accuracy for the Dirichlet–Neumann map, J. Comput. Phys. 228 (2009), pp. 2578–2576, Section 3], which is based on Fredholm integral equations and Nyström discretization schemes. Then, for stability reasons, to solve the discretized integral equation we use the method of smoothing projection introduced in [J. Helsing and B.T. Johansson, Fast reconstruction of harmonic functions from Cauchy data using integral equation techniques, Inverse Probl. Sci. Eng. 18 (2010), pp. 381–399, Section 7], which makes it possible to solve the discretized operator equation in a stable way with minor computational cost and high accuracy. With this approach, for sufficiently smooth Cauchy data, the normal derivative can also be accurately computed on the part of the boundary where no data is initially given.

Fast reconstruction of harmonic functions from Cauchy data using integral equation techniques

We consider the problem of stable determination of a harmonic function from knowledge of the solution and its normal derivative on a part of the boundary of the (bounded) solution domain. The alternating method is a procedure to generate an approximation to the harmonic function from such Cauchy data and we investigate a numerical implementation of this procedure based on Fredholm integral equations and Nyström discretization schemes, which makes it possible to perform a large number of iterations (millions) with minor computational cost (seconds) and high accuracy. Moreover, the original problem is rewritten as a fixed point equation on the boundary, and various other direct regularization techniques are discussed to solve that equation. We also discuss how knowledge of the smoothness of the data can be used to further improve the accuracy. Numerical examples are presented showing that accurate approximations of both the solution and its normal derivative can be obtained with much less computational time than in previous works.

Stability analysis of tree structured decision functions

In multicriteria decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. We propose here a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies. © 2003 Elsevier B.V. All rights reserved.

Calculating the derivative of piecewise functions

Exercises involving the calculation of the derivative of piecewise defined functions are common in calculus, with the aim of consolidating beginners’ knowledge of applying the definition of the derivative. In such exercises, the piecewise function is commonly made up of two smooth pieces joined together at one point. A strategy which avoids using the definition of the derivative is to find the derivative function of each smooth piece and check whether these functions agree at the chosen point. Showing that this strategy works together with investigating discontinuities of the derivative is usually beyond a calculus course. However, we shall show that elementary arguments can be used to clarify the calculation and behaviour of the derivative for piecewise functions.