Decoherence of Einstein-Podolsky-Rosen steering

We consider two systems A and B that share Einstein-Podolsky-Rosen (EPR) steering correlations and study how these correlations will decay, when each of the systems are independently coupled to a reservoir. EPR steering is a directional form of entanglement, and the measure of steering can change depending on whether the system A is steered by B, or vice versa. First, we examine the decay of the steering correlations of the two-mode squeezed state. We find that if the system B is coupled to a reservoir, then the decoherence of the steering of A by B is particularly marked, to the extent that there is a sudden death of steering after a finite time. We find a different directional effect, if the reservoirs are thermally excited. Second, we study the decoherence of the steering of a Schr\"odinger cat state, modeled as the entangled state of a spin and harmonic oscillator, when the macroscopic system (the cat) is coupled to a reservoir

Quantifying wind and pressure effects on trace gas fluxes across the soil–atmosphere interface

Acknowledgements. We would like to acknowledge the manufacturers of the inner toroid: Mark Bentley and Steve Howarth from the University of York, Dept. of Biology, mechanical and electronics workshops respectively. Furthermore, we would like to acknowledge the Forestry Commission for access and aid at Wheldrake Forest, Mike Bailey and Natural Resources Wales for access and assistance at Cors Fochno, and Norrie Russell and the Royal Society for the Protection of Birds for access and aid at Forsinard. We would also like to thank Graham Hambley, James Robinson, and Elizabeth Donkin for equipment preparation and sampling. Phil Ineson is thanked for the loan of essential equipment, site suggestions, and accessible power supply. Funding was provided by the University of York, Dept. of Biology, and by a grant to YAT by the UK Natural Environment Research Council (NE/H01182X/1).Peer reviewedPublisher PD

Real-time information processing of environmental sensor network data using Bayesian Gaussian processes

In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored, and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently reuses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered

The 2011 Retrovirology Prize winner Masao Matsuoka: forward looking and antisense

Masao Matsuoka wins the 2011 Retrovirology Prize

Retrovirology: 3 at age 2

Retrovirology announces new editorial board members and reprises progress over the first two years of publishing

Statistical tests for large tree-structured data

We develop a general statistical framework for the analysis and inference of large tree-structured data, with a focus on developing asymptotic goodness-of-fit tests. We first propose a consistent statistical model for binary trees, from which we develop a class of invariant tests. Using the model for binary trees, we then construct tests for general trees by using the distributional properties of the Continuum Random Tree, which arises as the invariant limit for a broad class of models for tree-structured data based on conditioned Galton–Watson processes. The test statistics for the goodness-of-fit tests are simple to compute and are asymptotically distributed as χ2 and F random variables. We illustrate our methods on an important application of detecting tumour heterogeneity in brain cancer. We use a novel approach with tree-based representations of magnetic resonance images and employ the developed tests to ascertain tumor heterogeneity between two groups of patients

Characterisation of a highly potent and near pan-neutralising anti-HIV monoclonal antibody expressed in tobacco plants

Background HIV remains one of the most important health issues worldwide, with almost 40 million people living with HIV. Although patients develop antibodies against the virus, its high mutation rate allows evasion of immune responses. Some patients, however, produce antibodies that are able to bind to, and neutralise different strains of HIV. One such ‘broadly neutralising’ antibody is ‘N6’. Identified in 2016, N6 can neutralise 98% of HIV-1 isolates with a median IC50 of 0.066 µg/mL. This neutralisation breadth makes N6 a very promising therapeutic candidate. Results N6 was expressed in a glycoengineered line of N. benthamiana plants (pN6) and compared to the mammalian cell-expressed equivalent (mN6). Expression at 49 mg/kg (fresh leaf tissue) was achieved in plants, although extraction and purification are more challenging than for most plant-expressed antibodies. N-glycoanalysis demonstrated the absence of xylosylation and a reduction in α(1,3)-fucosylation that are typically found in plant glycoproteins. The N6 light chain contains a potential N-glycosylation site, which was modified and displayed more α(1,3)-fucose than the heavy chain. The binding kinetics of pN6 and mN6, measured by surface plasmon resonance, were similar for HIV gp120. pN6 had a tenfold higher affinity for FcγRIIIa, which was reflected in an antibody-dependent cellular cytotoxicity assay, where pN6 induced a more potent response from effector cells than that of mN6. pN6 demonstrated the same potency and breadth of neutralisation as mN6, against a panel of HIV strains. Conclusions The successful expression of N6 in tobacco supports the prospect of developing a low-cost, low-tech production platform for a monoclonal antibody cocktail to control HIV in low-to middle income countries

Engineering Secretory IgA against Infectious Diseases

The dawn of antibody therapy was heralded by the rise of IgG therapeutics. However, other antibody classes are at our disposal—one of the most exciting is IgA and is the most abundant antibody class within humans. Unlike IgG, it is uniquely specialized for mucosal applications due to its ability to form complex Secretory IgA (SIgA) molecules. Since the mucosa is constantly exposed to potential infectious agents, SIgA is pivotal to disease prevention as an important component of the mucosal barrier. Compared to IgG, SIgA has proven superior effectiveness in mucosal surfaces, such as the airway epithelium or the harsh gut environment. Despite this, hurdles associated with low yield and challenging purification have blocked SIgA therapeutic advancement. However, as a result of new antibody engineering strategies, we are approaching the next generation of (IgA-based) antibody therapies. Strategies include fine-tuning SIgA assembly, exploring different production platforms, genetic engineering to improve purification, and glycoengineering of different components. Due to its stability in mucosal environments, SIgA therapeutics would revolutionize passive mucosal immunotherapy—an avenue still underexploited by current therapeutics. This chapter will focus on the current perspectives of SIgA engineering and explore different approaches to unlocking the full therapeutic potential of SIgAs