The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model

Automated adaptive analysis of tagged magnetic resonance images of the mouse heart

The full potential of tagged MRI of the mouse heart for non-invasive evaluation of cardiac mechanics in transgenic animals has not been realized due to excessive user involvement with available image processing algorithms. Therefore, we developed an automated, rapid, high-resolution analysis technique, called High Density Mapping (HDM), that uses spectral correlation to efficiently quantify regional wall deformation, does not entail tracking of individual tags, and involves minimal user interaction. HDM analysis distinguishes regional mechanics in healthy and infarcted mice within 2 minutes. This new method may help promote the practical use of tagged MRI in mice and other species.published_or_final_versio

A unified wavelet-based modelling framework for non-linear system identification: the WANARX model structure

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for non-linear system identification. A non-linear model, which is often represented using a multivariate non-linear function, is initially decomposed into a number of functional components via the wellknown analysis of variance (ANOVA) expression, which can be viewed as a special form of the NARX (non-linear autoregressive with exogenous inputs) model for representing dynamic input–output systems. By expanding each functional component using wavelet decompositions including the regular lattice frame decomposition, wavelet series and multiresolution wavelet decompositions, the multivariate non-linear model can then be converted into a linear-in-theparameters problem, which can be solved using least-squares type methods. An efficient model structure determination approach based upon a forward orthogonal least squares (OLS) algorithm, which involves a stepwise orthogonalization of the regressors and a forward selection of the relevant model terms based on the error reduction ratio (ERR), is employed to solve the linear-in-the-parameters problem in the present study. The new modelling structure is referred to as a wavelet-based ANOVA decomposition of the NARX model or simply WANARX model, and can be applied to represent high-order and high dimensional non-linear systems

Cellular modelling of Alström syndrome in human primary dermal fibroblasts and derived cells

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Advertising to Early Trend Propagators: Evidence from Twitter

In the digital economy, influencing and controlling the spread of information is a key concern for firms. One way firms try to achieve this is to target firm communications to consumers who embrace and propagate the spread of new information on emerging and `trending' topics on social media. However, little is known about whether early trend propagators are indeed responsive to firm-sponsored messages. To explore whether early propagators of trending topics respond to advertising messages, we use data from two field tests conducted by a charity and an emerging fashion firm on the micro-blogging service Twitter. On Twitter, 'promoted tweets' allow advertisers to target individuals based on the content of their recent postings. Twitter continuously identifies in real time which topics are newly popular among Twitter users. In the field tests, we collaborated with a charity and a fashion firm to target ads at consumers who embraced a Twitter trend early in its life-cycle by posting about it, and compared their behavior to that of consumers who posted about the same topic only later on. Throughout both field tests, we consistently find that early propagators of trends are less responsive to advertising than consumers who embrace trends later

Small inhibitor of Bcl-2, HA14-1, selectively enhanced the apoptotic effect of cisplatin by modulating Bcl-2 family members in MDA-MB-231 breast cancer cells

Inhibition or downregulation of Bcl-2 represents a new therapeutic approach to by-pass chemoresistance in cancer cells. Therefore, we explored the potential of this approach in breast cancer cells. Cisplatin and paclitaxel induced apoptosis in a dose-dependent manner in MCF-7 (drug-sensitive) and MDA-MB-231 (drug-insensitive) cells. Furthermore, when we transiently silenced Bcl-2, both cisplatin and paclitaxel induced apoptosis more than parental cells. Dose dependent induction of apoptosis by drugs was enhanced by the pre-treatment of these cells with HA14-1, a Bcl-2 inhibitor. Although the effect of cisplatin was significant on both cell lines, the effect of paclitaxel was much less potent only in MDA-MB-231 cells. To further understand the distinct role of drugs in MDA-MB-231 cells pretreated with HA14-1, caspases and Bcl-2 family proteins were studied. The apoptotic effect of cisplatin with or without HA14-1 pre-treatment is shown to be caspase-dependent. Among pro-apoptotic Bcl-2 proteins, Bax and Puma were found to be up-regulated whereas Bcl-2 and Bcl-x(L) were down-regulated when cells were pretreated with HA14-1 followed by paclitaxel or cisplatin. Enforced Bcl-2 expression in MDA-MB-231 cells abrogated the sensitizing effect of HA14-1 in cisplatin induced apoptosis. These results suggest that the potentiating effect of HA14-1 is drug and cell type specific and may not only depend on the inhibition of Bcl-2. Importantly, alteration of other pro-apoptotic or anti-apoptotic Bcl-2 family members may dictate the apoptotic response when HA14-1 is combined with chemotherapeutic drugs

Towards an Efficient Finite Element Method for the Integral Fractional Laplacian on Polygonal Domains

We explore the connection between fractional order partial differential equations in two or more spatial dimensions with boundary integral operators to develop techniques that enable one to efficiently tackle the integral fractional Laplacian. In particular, we develop techniques for the treatment of the dense stiffness matrix including the computation of the entries, the efficient assembly and storage of a sparse approximation and the efficient solution of the resulting equations. The main idea consists of generalising proven techniques for the treatment of boundary integral equations to general fractional orders. Importantly, the approximation does not make any strong assumptions on the shape of the underlying domain and does not rely on any special structure of the matrix that could be exploited by fast transforms. We demonstrate the flexibility and performance of this approach in a couple of two-dimensional numerical examples

Evaluation of Phage Display Discovered Peptides as Ligands for Prostate-Specific Membrane Antigen (PSMA)

The aim of this study was to identify potential ligands of PSMA suitable for further development as novel PSMA-targeted peptides using phage display technology. The human PSMA protein was immobilized as a target followed by incubation with a 15-mer phage display random peptide library. After one round of prescreening and two rounds of screening, high-stringency screening at the third round of panning was performed to identify the highest affinity binders. Phages which had a specific binding activity to PSMA in human prostate cancer cells were isolated and the DNA corresponding to the 15-mers were sequenced to provide three consensus sequences: GDHSPFT, SHFSVGS and EVPRLSLLAVFL as well as other sequences that did not display consensus. Two of the peptide sequences deduced from DNA sequencing of binding phages, SHSFSVGSGDHSPFT and GRFLTGGTGRLLRIS were labeled with 5-carboxyfluorescein and shown to bind and co-internalize with PSMA on human prostate cancer cells by fluorescence microscopy. The high stringency requirements yielded peptides with affinities KD∼1 μM or greater which are suitable starting points for affinity maturation. While these values were less than anticipated, the high stringency did yield peptide sequences that apparently bound to different surfaces on PSMA. These peptide sequences could be the basis for further development of peptides for prostate cancer tumor imaging and therapy. © 2013 Shen et al

Wall Crossing and Instantons in Compactified Gauge Theory

We calculate the leading weak-coupling instanton contribution to the moduli-space metric of N=2 supersymmetric Yang-Mills theory with gauge group SU(2) compactified on R^3 x S^1. The results are in precise agreement with the semiclassical expansion of the exact metric recently conjectured by Gaiotto, Moore and Neitzke based on considerations related to wall-crossing in the corresponding four-dimensional theory.Comment: 24 pages, no figure

Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance