Case series: 2q33.1 microdeletion syndrome--further delineation of the phenotype.

Recurrent deletions of 2q32q33 have recently been reported as a new microdeletion syndrome, clinical features of which include significant learning difficulties, growth retardation, dysmorphic features, thin and sparse hair, feeding difficulties, and cleft or high palate. Haploinsufficiency of one gene within the deleted region, SATB2, has been suggested to be responsible for most of the features of the syndrome. This article describes seven previously unreported patients with deletions at 2q33.1, all partially overlapping the previously described critical region for the 2q33.1 microdeletion syndrome. The deletions ranged in size from 35 kb to 10.4 Mb, with the smallest deletion entirely within the SATB2 gene. Patients demonstrated significant developmental delay and challenging behaviour, a particular behavioural phenotype that seems to be emerging with more reported patients with this condition. One patient in this cohort has a deletion entirely within SATB2 and has a cleft palate, whereas several patients with larger deletions have a high arched palate. In addition, one other patient has significant orthopaedic problems with ligamentous laxity. Interestingly, this patient has a deletion that lies just distal to SATB2. The orthopaedic problems have not been reported previously and are possibly an additional feature of this syndrome. Overall, this report provides further evidence that the SATB2 gene is the critical gene in this microdeletion syndrome. In addition, because the individuals in this study range in age from 3-19 years, these patients will help define the natural progression of the phenotype in patients with this microdeletion.

Clinical laboratory practices for the detection of rotavirus in England and Wales: can surveillance based on routine laboratory testing data be used to evaluate the impact of vaccination?

Two rotavirus vaccines have recently been licensed in Europe. Rotavirus surveillance data in many European countries are based on reports of laboratory-confirmed rotavirus infections. If surveillance data based on routine laboratory testing data are to be used to evaluate the impact of vaccination programmes, it is important to determine how the data are influenced by differences in testing practices, and how these practices are likely to affect the ability of the surveillance data to represent trends in rotavirus disease in the community. We conducted a survey of laboratory testing policies for rotavirus gastroenteritis in England and Wales in 2008. 60% (94/156) of laboratories responded to the survey. 91% of reporting laboratories offered routine testing for rotavirus all year round and 89% of laboratories offered routine rotavirus testing of all stool specimens from children under the age of five years. In 96% of laboratories, rotavirus detection was presently done either by rapid immunochromatographic tests or by enzyme-linked immunosorbent assay. Currently, rotavirus testing policies among laboratories in England and Wales are relatively homogenous. Therefore, surveillance based on laboratory testing data is likely to be representative of rotavirus disease trends in the community in the most frequently affected age groups (children under the age of five years) and could be used to help determine the impact of a rotavirus vaccine.

Adaptive nonlinear least bit error-rate detection for symmetrical RBF beamforming.

A powerful symmetrical radial basis function (RBF) aided detector is proposed for nonlinear detection in so-called rank-deficient multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, the proposed RBF detector becomes capable of approaching the optimal Bayesian detection performance using channel-impaired training data. A novel nonlinear least bit error algorithm is derived for adaptive training of the symmetrical RBF detector based on a stochastic approximation to the Parzen window estimation of the detector output's probability density function. The proposed adaptive solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting four users with the aid of two receive antennas or seven users employing four receive antenna elements.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure.

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers that generalize well.

The effect of immunomodulatory treatment on multiple sclerosis fatigue.

OBJECTIVE: To assess the effects of glatiramer acetate and beta interferon on fatigue in multiple sclerosis. METHODS: Fatigue was measured at baseline and six months using the fatigue impact scale (FIS). Groups (glatiramer acetate and beta interferon) were evaluated for the proportion improved, using Fisher's exact test. Logistic regression analysis assessed the relation between treatment group and improvement and controlled for confounding variables. RESULTS: Six month paired FIS assessments were available for 218 patients (76% female). Ages ranged between 19 and 61 years, with 86% having relapsing-remitting disease. Glatiramer acetate was used by 61% and beta interferon by 39%. At baseline, total FIS and subscale scores were comparable in the two groups. More patients improved on glatiramer acetate than on beta interferon on total FIS (24.8% v 12.9%, p = 0.033; adjusted odds ratio = 2.36, 95% confidence interval 1.03 to 5.42), and on physical (28.6% v 14.1%, p = 0.013) and cognitive subscales (21.1% v 10.6%, p = 0.045). Logistic regression analysis confirmed the association between glatiramer acetate use and improved fatigue, after accounting for baseline group differences. CONCLUSIONS: The odds of reduced multiple sclerosis fatigue were around twice as great with glatiramer acetate treatment as with beta interferon. Confirmation of this result is required.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design.

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Renal effects of cyclooxygenase-2 selective inhibitors.

Recent in-vitro and animal data show that cyclooxygenase-2 has an integral role in the physiology and pathophysiology of the kidney. Cyclooxygenase-2 regulates renin-angiotensin secretion, and thereby glomerular filtration rate and sodium homeostasis. It is also important for protecting against hypertonic stress. As a consequence, it is not surprising that clinical data verify that selective inhibitors of cyclooxygenase-2 affect renal function to a degree similar to that which has previously been documented with nonselective nonsteroidal anti-inflammatory drugs.

On a class of support vector kernels based on frames in function Hilbert spaces.

There has been an increasing interest in kernel-based techniques, such as support vector techniques, regularization networks, and gaussian processes. There are inner relationships among those techniques, with the kernel function playing a central role. This article discusses a new class of kernel functions derived from the so-called frames in a function Hilbert space.

A hybrid learning scheme combining EM and MASMOD algorithms for fuzzy local linearization modeling.

Fuzzy local linearization (FLL) is a useful divide-and-conquer method for coping with complex problems such as modeling unknown nonlinear systems from data for state estimation and control. Based on a probabilistic interpretation of FLL, the paper proposes a hybrid learning scheme for FLL modeling, which uses a modified adaptive spline modeling (MASMOD) algorithm to construct the antecedent parts (membership functions) in the FLL model, and an expectation-maximization (EM) algorithm to parameterize the consequent parts (local linear models). The hybrid method not only has an approximation ability as good as most neuro-fuzzy network models, but also produces a parsimonious network structure (gain from MASMOD) and provides covariance information about the model error (gain from EM) which is valuable in applications such as state estimation and control. Numerical examples on nonlinear time-series analysis and nonlinear trajectory estimation using FLL models are presented to validate the derived algorithm.

Nonlinear model structure detection using optimum experimental design and orthogonal least squares.

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.

Piecewise Lyapunov stability conditions of fuzzy systems.

In this paper we address the stability of a class of non-linear fuzzy systems that can be decomposed into a set of local models characterized as Takagi-Sugeno models. This new approach includes a consideration of the input membership functions. Via this approach, a reduction in the number of candidate Lyapunov functions and associated linear matrix inequalities (LMIs) is produced. This approach significantly reduces the computational load associated with determining closed loop stability as the input dimension increases.

The relevance vector machine technique for channel equalization application.

The relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique.

Bioactive beta-bend structures for the antagonist halpha CGRP(8 - 37) at the CGRP(1) receptor of the rat pulmonary artery.

The aim of this study was to determine beta-bend structures and the role of the N- and C-terminus in the antagonist halpha CGRP(8 - 37) at the rat pulmonary artery CGRP receptor mediating halpha CGRP relaxation. Halpha CGRP(8 - 37) Pro(16) (10(-6) M), with a bend-biasing residue (proline) at position 16, did not antagonize halpha CGRP responses, while a structure-conserving amino acid (alanine(16)) at the same position retained antagonist activity (apparent pK(B) 6.6+/-0.1; 10(-6) M). Halpha CGRP(8 - 37) Pro(19) (10(-6) M), with proline at position 19 was an antagonist (apparent pK(B) 6.9+/-0.1). Incorporation of a beta-bend forcing residue, BTD (beta-turn dipeptide), at positions 19 and 20 in halpha CGRP(8 - 37) (10(-6) M) antagonized halpha CGRP responses (apparent pK(B) 7.2+/-0.2); and BTD at positions 19,20 and 33,34 within halpha CGRP(8 - 37) was a competitive antagonist (pA(2) 7.2; Schild plot slope 1.0+/-0.1). Halpha CGRP(8 - 37) analogues, substituted at the N-terminus by either glycine(8) or des-NH(2) valine(8) or proline(8) were all antagonists (apparent pK(B) 6.9+/-0.1; (10(-6) M), 7.0+/-0.1 (10(-6) M), and pA(2) 7.0 (slope 1.0+/-0.2), respectively); while replacements by proline(8) together with glutamic acid(10,14) in halpha CGRP(8 - 37) (10(-6) M) or alanine amide(37) at the C-terminus of halpha CGRP(8 - 37) (10(-5) M) were both inactive compounds. In conclusion, possible bioactive structures of halpha CGRP(8 - 37) include two beta-bends (at 18 - 21 and 32 - 35), which were mimicked by BTD incorporation. Within halpha CGRP(8 - 37), the N-terminus is not essential for antagonism while the C-terminus may interact directly with CGRP(1) receptors in the rat pulmonary artery.

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Conformational restraints revealing bioactive beta-bend structures for halpha CGRP8-37 at the CGRP2 receptor of the rat prostatic vas deferens.

1. The main aim of this study was to identify putative beta-bends and the role of the N- and C-terminus in the CGRP receptor antagonist halpha CGRP8-37, which was measured against halpha CGRP inhibition of twitch responses in the rat prostatic vas deferens. 2. With a bend-biasing residue (proline) at position 16 in halpha CGRP8-37 (10(-5) M) an inactive compound was produced, while alanine at the same position retained antagonist activity (apparent pKB 5.6+/-0.1 at 10(-5) M). Proline at position 19 within halpha CGRP8-37 (10(-5) M) was an antagonist (apparent pKB 5.8+/-0.1). 3. Incorporation of a bend-forcing structure (beta-turn dipeptide or BTD) at either positions 19,20 or 33,34 in halpha CGRP8-37 (10(-5) M) antagonized halpha CGRP responses (apparent pKB 6.0+/-0.1 and 6.1+/-0.1, respectively). Replacement by BTD at both positions 19,20 and 33,34 within halpha CGRP8-37 competitively antagonized responses to halpha CGRP (pA2 6.2; Schild plot slope 1.0+/-0.1). 4. Halpha CGRP8-37 analogues (10(-5) M), substituted at the N-terminus by either glycine8, or des-NH2 valine8 or proline8 were all antagonists against halpha CGRP (apparent pKB 6.1+/-0.1, 6.5+/-0.1 and 6.1+/-0.1, respectively), while halpha CGRP8-37 (10(-5) M) substituted in three places by proline8 and glutamic acid10,14 was inactive. 5. Replacement of the C-terminus by alanine amide37 in halpha CGRP8-37 (10(-5) M) failed to antagonize halpha CGRP responses. 6. Peptidase inhibitors did not alter either the agonist potency of halpha CGRP or the antagonist affinities of halpha CGRP8-37 BTD19,20 and 33,34 and halpha CGRP8-37 Gly8 (against halpha CGRP responses). 7. In conclusion, two beta-bends at positions 18-21 and 32-35 are compatible with high affinity by BTD and is the first approach of modelling the bioactive structure of halpha CGRP8-37. Further, the N-terminus of halpha CGRP8-37 is not essential for antagonism, while the C-terminus interacts directly with CGRP receptor binding sites of the rat vas deferens.

Fuzzy local linearization and local basis function expansion in nonlinear system modeling.

Fuzzy local linearization is compared with local basis function expansion for modeling unknown nonlinear processes. First-order Takagi-Sugeno fuzzy model and the analysis of variance (ANOVA) decomposition are combined for the fuzzy local linearization of nonlinear systems, in which B-splines are used as membership functions of the fuzzy sets for input space partition. A modified algorithm for adaptive spline modeling of observation data (MASMOD) is developed for determining the number of necessary B-splines and their knot positions to achieve parsimonious models. This paper illustrates that fuzzy local linearization models have several advantages over local basis function expansion based models in nonlinear system modeling.

Linearization and state estimation of unknown discrete-time nonlinear dynamic systems using recurrent neurofuzzy networks.

Model-based methods for the state estimation and control of linear systems have been well developed and widely applied. In practice, the underlying systems are often unknown and nonlinear. Therefore, data based model identification and associated linearization techniques are very important. Local linearization and feedback linearization have drawn considerable attention in recent years. In this paper, linearization techniques using neural networks are reviewed, together with theoretical difficulties associated with the application of feedback linearization. A recurrent neurofuzzy network with an analysis of variance (ANOVA) decomposition structure and its learning algorithm are proposed for linearizing unknown discrete-time nonlinear dynamic systems. It can be viewed as a method for approximate feedback linearization, as such it enlarges the class of nonlinear systems that can be feedback linearized using neural networks. Applications of this new method to state estimation are investigated with realistic simulation examples, which shows that the new method has useful practical properties such as model parametric parsimony and learning convergence, and is effective in dealing with complex unknown nonlinear systems.