Stress concentrations around voids in three dimensions : The roots of failure

Funding This work forms part of a NERC New Investigator award for DH (NE/I001743/1), which is gratefully acknowledged. Acknowledgments The authors would like to acknowledge the reviewers, Elizabeth Ritz and Phillip Resor. Their reviews were very constructive, both helping to improve the manuscripts consistency and highlighting a number of errors in the initial submission. The authors would also like to thank Lydia Jagger's keen eye and patience, she helped greatly in removing a number of grammatical errors from the initial draft.Peer reviewedPublisher PD

Structural basis of complement membrane attack complex formation

In response to complement activation, the membrane attack complex (MAC) assembles from fluid-phase proteins to form pores in lipid bilayers. MAC directly lyses pathogens by a ‘multi-hit’ mechanism; however, sublytic MAC pores on host cells activate signalling pathways. Previous studies have described the structures of individual MAC components and subcomplexes; however, the molecular details of its assembly and mechanism of action remain unresolved. Here we report the electron cryo-microscopy structure of human MAC at subnanometre resolution. Structural analyses define the stoichiometry of the complete pore and identify a network of interaction interfaces that determine its assembly mechanism. MAC adopts a ‘split-washer’ configuration, in contrast to the predicted closed ring observed for perforin and cholesterol-dependent cytolysins. Assembly precursors partially penetrate the lipid bilayer, resulting in an irregular β-barrel pore. Our results demonstrate how differences in symmetric and asymmetric components of the MAC underpin a molecular basis for pore formation and suggest a mechanism of action that extends beyond membrane penetration

Therapeutic impact of 2-[fluorine-18]fluoro-2-deoxy-D-glucose positron emission tomography in the pre- and postoperative staging of patients with clinically intermediate or high-risk breast cancer

Background: Positron emission tomography with 2-[fluorine-18]fluoro-2-deoxy-D-glucose (FDG-PET) is an accurate imaging modality for the staging of breast cancer. The aim of this study was to determine the potential therapeutic impact of pre- and postoperative FDG-PET in patients with clinically intermediate or high-risk breast cancer. Patients and methods: One hundred and fourteen patients with newly diagnosed breast cancer were examined before (73) or after (41) surgery. Patient data were translated into three scoring sheets corresponding to information available before positron emission tomography (PET), after PET and after further diagnostic tests. Three medical oncologists independently reviewed the retrospectively acquired patient data and prospectively made decisions on the theoretically planed treatment for each time point, according to the recommendations of St Gallen Consensus Guidelines 2005. Results: FDG-PET changed the planed treatment in 32% of 114 patients. In 20% of cases, therapeutic intention (curative versus palliative) was modified. Radiation treatment planning was changed in 27%, surgical planning in 9%, chemotherapy in 11% and intended therapy with bisphosphonates in 13% of all patients. Conclusion: Based on current treatment guidelines, FDG-PET, as a staging procedure in patients with newly diagnosed clinically intermediate or high-risk breast cancer examined pre- and postoperatively, may have a substantial therapeutic impact on treatment plannin

Analysis of Different Types of Regret in Continuous Noisy Optimization

The performance measure of an algorithm is a crucial part of its analysis. The performance can be determined by the study on the convergence rate of the algorithm in question. It is necessary to study some (hopefully convergent) sequence that will measure how "good" is the approximated optimum compared to the real optimum. The concept of Regret is widely used in the bandit literature for assessing the performance of an algorithm. The same concept is also used in the framework of optimization algorithms, sometimes under other names or without a specific name. And the numerical evaluation of convergence rate of noisy algorithms often involves approximations of regrets. We discuss here two types of approximations of Simple Regret used in practice for the evaluation of algorithms for noisy optimization. We use specific algorithms of different nature and the noisy sphere function to show the following results. The approximation of Simple Regret, termed here Approximate Simple Regret, used in some optimization testbeds, fails to estimate the Simple Regret convergence rate. We also discuss a recent new approximation of Simple Regret, that we term Robust Simple Regret, and show its advantages and disadvantages.Comment: Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States. 201

On the Prior Sensitivity of Thompson Sampling

The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algorithm is that it allows domain knowledge to be conveniently encoded as a prior distribution to balance exploration and exploitation more effectively. While it is generally believed that the algorithm's regret is low (high) when the prior is good (bad), little is known about the exact dependence. In this paper, we fully characterize the algorithm's worst-case dependence of regret on the choice of prior, focusing on a special yet representative case. These results also provide insights into the general sensitivity of the algorithm to the choice of priors. In particular, with $p$ being the prior probability mass of the true reward-generating model, we prove $O(\sqrt{T/p})$ and $O(\sqrt{(1-p)T})$ regret upper bounds for the bad- and good-prior cases, respectively, as well as \emph{matching} lower bounds. Our proofs rely on the discovery of a fundamental property of Thompson Sampling and make heavy use of martingale theory, both of which appear novel in the literature, to the best of our knowledge.Comment: Appears in the 27th International Conference on Algorithmic Learning Theory (ALT), 201

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization

International audienceThe use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds or sometimes even dozens of possible actions, and therefore cannot be applied to many real-world problems. We consider the RL problem in the supervised classification framework where the optimal policy is obtained through a multiclass classifier, the set of classes being the set of actions of the problem. We introduce error-correcting output codes (ECOCs) in this setting and propose two new methods for reducing complexity when using rollouts-based approaches. The first method consists in using an ECOC-based classifier as the multiclass classifier, reducing the learning complexity from O(A2) to O(Alog(A)) . We then propose a novel method that profits from the ECOC's coding dictionary to split the initial MDP into O(log(A)) separate two-action MDPs. This second method reduces learning complexity even further, from O(A2) to O(log(A)) , thus rendering problems with large action sets tractable. We finish by experimentally demonstrating the advantages of our approach on a set of benchmark problems, both in speed and performance

Brownian motion exhibiting absolute negative mobility

We consider a single Brownian particle in a spatially symmetric, periodic system far from thermal equilibrium. This setup can be readily realized experimentally. Upon application of an external static force F, the average particle velocity is negative for F>0 and positive for F<0 (absolute negative mobility).Comment: 4 pages, 3 figures, to be published in PR

Transition Between Ground State and Metastable States in Classical 2D Atoms

Structural and static properties of a classical two-dimensional (2D) system consisting of a finite number of charged particles which are laterally confined by a parabolic potential are investigated by Monte Carlo (MC) simulations and the Newton optimization technique. This system is the classical analog of the well-known quantum dot problem. The energies and configurations of the ground and all metastable states are obtained. In order to investigate the barriers and the transitions between the ground and all metastable states we first locate the saddle points between them, then by walking downhill from the saddle point to the different minima, we find the path in configurational space from the ground state to the metastable states, from which the geometric properties of the energy landscape are obtained. The sensitivity of the ground-state configuration on the functional form of the inter-particle interaction and on the confinement potential is also investigated

Effects of deletion of the Streptococcus pneumoniae lipoprotein diacylglyceryl transferase gene lgt on ABC transporter function and on growth in vivo

Lipoproteins are an important class of surface associated proteins that have diverse roles and frequently are involved in the virulence of bacterial pathogens. As prolipoproteins are attached to the cell membrane by a single enzyme, prolipoprotein diacylglyceryl transferase (Lgt), deletion of the corresponding gene potentially allows the characterisation of the overall importance of lipoproteins for specific bacterial functions. We have used a Δlgt mutant strain of Streptococcus pneumoniae to investigate the effects of loss of lipoprotein attachment on cation acquisition, growth in media containing specific carbon sources, and virulence in different infection models. Immunoblots of triton X-114 extracts, flow cytometry and immuno-fluorescence microscopy confirmed the Δlgt mutant had markedly reduced lipoprotein expression on the cell surface. The Δlgt mutant had reduced growth in cation depleted medium, increased sensitivity to oxidative stress, reduced zinc uptake, and reduced intracellular levels of several cations. Doubling time of the Δlgt mutant was also increased slightly when grown in medium with glucose, raffinose and maltotriose as sole carbon sources. These multiple defects in cation and sugar ABC transporter function for the Δlgt mutant were associated with only slightly delayed growth in complete medium. However the Δlgt mutant had significantly reduced growth in blood or bronchoalveolar lavage fluid and a marked impairment in virulence in mouse models of nasopharyngeal colonisation, sepsis and pneumonia. These data suggest that for S. pneumoniae loss of surface localisation of lipoproteins has widespread effects on ABC transporter functions that collectively prevent the Δlgt mutant from establishing invasive infection