Recombinant anti-EspA antibodies block Escherichia coli O157:H7-induced attaching and effacing lesions in vitro

Intimin and EspA proteins are virulence factors expressed by attaching and effacing Escherichia coli (AEEC) such as enteropathogenic and enterohaemorrhagic E. coli. The EspA protein makes up a filament structure forming part of the type III secretion system (TTSS) that delivers effector proteins to the host epithelial cell. Bacterial surface displayed intimin interacts with translocated intimin receptor in the host cell membrane leading to intimate attachment of the bacterium and subsequent attaching and effacing lesions. Here, we have assessed the use of recombinant monoclonal antibodies against E. coli O157:147 EspA and intimin for the disruption of AEEC interaction with the host cell. Anti-gamma intimin antibodies did not reduce either adhesion of E. coli O157:H7 to host cell mono-layers or subsequent host cell actin rearrangement. Anti-EspA antibodies similarly had no effect on bacterial adhesion however they had a marked effect upon E. coli O157:H7-induced host cell actin rearrangement, where both monoclonal and polyclonal antibodies completely blocked cytoskeletal changes within the host cell. Furthermore, these anti-EspA antibodies were shown to reduce actin rearrangement induced by some but not all other AEEC serotypes tested. Both polyclonal and monoclonal antibodies could be used to label E. coli O157 EspA filaments and these immunoreagents did not inhibit the formation of such filaments. This is the first report of monoclonal antibodies to EspA capable of disrupting the TTSS function of E. coli O157:H7. (c) 2005 Elsevier SAS. All rights reserved.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

Bis(azidophenyl)phosphole building block for extended π-conjugated systems

Novel bis(azidophenyl)phosphole sulfide building block 8 has been developed to give access to a plethora of phosphole-containing π-conjugated systems in a simple synthetic step. This was explored for the reaction of the two azido moieties with phenyl-, pyridyl- and thienylacetylenes, to give bis(aryltriazolyl)-extended π-systems, having either the phosphole sulfide (9) or the phosphole (10) group as central ring. These conjugated frameworks exhibit intriguing photophysical and electrochemical properties that vary with the nature of the aromatic end-group. The λ3-phospholes 10 display blue fluorescence (λem = 460–469 nm) with high quan-tum yield (ΦF = 0.134–0.309). The radical anion of pyridylsubstituted phosphole sulfide 9b was observed with UV/Vis spectroscopy. TDDFT calculations on the extended π-systems showed some variation in the shape of the HOMOs, which was found to have an effect on the extent of charge transfer, depending on the aromatic end-group. Some fine-tuning of the emission maxima was observed, albeit subtle, showing a decrease in conjugation in the order thienyl � phenyl � pyridyl. These results show that variations in the distal ends of such π-systems have a subtle but significant effect on photophysical properties.

Probability of survival in a random exchange economy with dependent agents

In this paper I analyze the general equilibrium in a random Walrasian economy. Dependence among agents is introduced in the form of dependency neighborhoods. Under the uncertainty, an agent may fail to survive due to a meager endowment in a particular state (direct effect), as well as due to unfavorable equilibrium price system at which the value of the endowment falls short of the minimum needed for survival (indirect terms-of-trade effect). To illustrate the main result I compute the stochastic limit of equilibrium price and probability of survival of an agent in a large Cobb-Douglas economy.

Tetragonal and helical morphologies from polyferrocenylsilane block polyelectrolytes via ionic self-assembly

The use of ionic self-assembly, a facile noncovalent approach, to access non-conventional block copolymer morphologies, including tetragonal and helical structures, from a combination of polyferrocenylsilane diblock copolymer polyelectrolytes and AOT-based surfactants, is described.

Validating the reported random errors of ACE‐FTS measurements

In order to validate the reported precision of space‐based atmospheric composition measurements, validation studies often focus on measurements in the tropical stratosphere, where natural variability is weak. The scatter in tropical measurements can then be used as an upper limit on single‐profile measurement precision. Here we introduce a method of quantifying the scatter of tropical measurements which aims to minimize the effects of short‐term atmospheric variability while maintaining large enough sample sizes that the results can be taken as representative of the full data set. We apply this technique to measurements of O3, HNO3, CO, H2O, NO, NO2, N2O, CH4, CCl2F2, and CCl3F produced by the Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE‐FTS). Tropical scatter in the ACE‐FTS retrievals is found to be consistent with the reported random errors (RREs) for H2O and CO at altitudes above 20 km, validating the RREs for these measurements. Tropical scatter in measurements of NO, NO2, CCl2F2, and CCl3F is roughly consistent with the RREs as long as the effect of outliers in the data set is reduced through the use of robust statistics. The scatter in measurements of O3, HNO3, CH4, and N2O in the stratosphere, while larger than the RREs, is shown to be consistent with the variability simulated in the Canadian Middle Atmosphere Model. This result implies that, for these species, stratospheric measurement scatter is dominated by natural variability, not random error, which provides added confidence in the scientific value of single‐profile measurements.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

Hepatitis C virus NS5A inhibits mixed lineage kinase 3 to block apoptosis

Hepatitis C virus (HCV) infection results in the activation of numerous stress responses including oxidative stress, with the potential to induce an apoptotic state. Previously we have shown that HCV attenuates the stress-induced, p38MAPK-mediated up-regulation of the K+ channel Kv2.1, to maintain the survival of infected cells in the face of cellular stress. We demonstrated that this effect was mediated by HCV non-structural 5A (NS5A) protein, which impaired p38MAPK activity through a polyproline motif dependent interaction, resulting in reduction of phosphorylation activation of Kv2.1. In this study, we investigated the host cell proteins targeted by NS5A in order to mediate Kv2.1 inhibition. We screened a phage-display library expressing the entire complement of human SH3 domains for novel NS5A-host cell interactions. This analysis identified mixed lineage kinase 3 (MLK3) as a putative NS5A interacting partner. MLK3 is a serine/threonine protein kinase that is a member of the MAPK kinase kinase (MAP3K) family and activates p38MAPK. An NS5A-MLK3 interaction was confirmed by co-immunoprecipitation and western blot analysis. We further demonstrate a novel role of MLK3 in the modulation of Kv2.1 activity, whereby MLK3 overexpression leads to the up-regulation of channel activity. Accordingly, coexpression of NS5A suppressed this stimulation. Additionally we demonstrate that overexpression of MLK3 induced apoptosis which was also counteracted by NS5A. We conclude that NS5A targets MLK3 with multiple downstream consequences for both apoptosis and K+ homeostasis.

Sparse polynomial approximation in positive order Sobolev spaces with bounded mixed derivatives and applications to elliptic problems with random loading

In the present paper we study the approximation of functions with bounded mixed derivatives by sparse tensor product polynomials in positive order tensor product Sobolev spaces. We introduce a new sparse polynomial approximation operator which exhibits optimal convergence properties in L2 and tensorized View the MathML source simultaneously on a standard k-dimensional cube. In the special case k=2 the suggested approximation operator is also optimal in L2 and tensorized H1 (without essential boundary conditions). This allows to construct an optimal sparse p-version FEM with sparse piecewise continuous polynomial splines, reducing the number of unknowns from O(p2), needed for the full tensor product computation, to View the MathML source, required for the suggested sparse technique, preserving the same optimal convergence rate in terms of p. We apply this result to an elliptic differential equation and an elliptic integral equation with random loading and compute the covariances of the solutions with View the MathML source unknowns. Several numerical examples support the theoretical estimates.

On the spectra and pseudospectra of a class of non-self-adjoint random matrices and operators

In this paper we develop and apply methods for the spectral analysis of non-selfadjoint tridiagonal infinite and finite random matrices, and for the spectral analysis of analogous deterministic matrices which are pseudo-ergodic in the sense of E. B. Davies (Commun. Math. Phys. 216 (2001), 687–704). As a major application to illustrate our methods we focus on the “hopping sign model” introduced by J. Feinberg and A. Zee (Phys. Rev. E 59 (1999), 6433–6443), in which the main objects of study are random tridiagonal matrices which have zeros on the main diagonal and random ±1’s as the other entries. We explore the relationship between spectral sets in the finite and infinite matrix cases, and between the semi-infinite and bi-infinite matrix cases, for example showing that the numerical range and p-norm ε - pseudospectra (ε > 0, p ∈ [1,∞] ) of the random finite matrices converge almost surely to their infinite matrix counterparts, and that the finite matrix spectra are contained in the infinite matrix spectrum Σ. We also propose a sequence of inclusion sets for Σ which we show is convergent to Σ, with the nth element of the sequence computable by calculating smallest singular values of (large numbers of) n×n matrices. We propose similar convergent approximations for the 2-norm ε -pseudospectra of the infinite random matrices, these approximations sandwiching the infinite matrix pseudospectra from above and below.

Does domperidone, a D2-antagonist alter gastric emptying rates and appetite sensations in healthy adults with high-fat meal? A block-randomised, single-blind placebo-controlled study

BACKGROUND: Accelerated gastric emptying (GE) may lead to reduced satiation, increased food intake and is associated with obesity and type 2 diabetes. Domperidone is a dopamine 2 (D(2)) receptor antagonist with claims of gastrointestinal tract pro-kinetic activity. In humans, domperidone is used as an anti-emetic and treatment for gastrointestinal bloating and discomfort. AIM: To determine the effect of acute domperidone administration on GE rate and appetite sensations in healthy adults. METHODS: A single-blind block randomised placebo-controlled crossover study assessed 13 healthy adults. Subjects ingested 10 mg domperidone or placebo 30 min before a high-fat (HF) test meal. GE rate was determined using the (13)CO(2) octanoic acid breath test. Breath samples and subjective appetite ratings were collected in the fasted and during the 360 min postprandial period. RESULTS:Gastric emptying half-time was similar following placebo (254 ± 54 min) and 10 mg domperidone (236 ± 65 min). Domperidone did not change appetite sensations during the 360 min postprandial period (P > 0.05). CONCLUSIONS: In healthy adults, acute administration of 10 mg domperidone did not change GE or appetite sensations following a HF test meal.

A scalable expressive ensemble learning using Random Prism: a MapReduce approach

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system.

Want to block earworms from conscious awareness?B(u)y gum!

Three experiments examine the role of articulatory motor planning in experiencing an involuntary musical recollection (an “earworm”). Experiment 1 shows that interfering with articulatory motor programming by chewing gum reduces both the number of voluntary and the number of involuntary—unwanted—musical thoughts. This is consistent with other findings that chewing gum interferes with voluntary processes such as recollections from verbal memory, the interpretation of ambiguous auditory images, and the scanning of familiar melodies, but is not predicted by theories of thought suppression, which assume that suppression is made more difficult by concurrent tasks or cognitive loads. Experiment 2 shows that chewing the gum affects the experience of “hearing” the music and cannot be ascribed to a general effect on thinking about a tune only in abstract terms. Experiment 3 confirms that the reduction of musical recollections by chewing gum is not the consequence of a general attentional or dual-task demand. The data support a link between articulatory motor programming and the appearance in consciousness of both voluntary and unwanted musical recollections.