Evolution of entanglement within classical light states

We investigate the evolution of quantum correlations over the lifetime of a multi-photon state. Measurements reveal time-dependent oscillations of the entanglement fidelity for photon pairs created by a single semiconductor quantum dot. The oscillations are attributed to the phase acquired in the intermediate, non-degenerate, exciton-photon state and are consistent with simulations. We conclude that emission of photon pairs by a typical quantum dot with finite polarisation splitting is in fact entangled in a time-evolving state, and not classically correlated as previously regarded

Giant Stark effect in the emission of single semiconductor quantum dots

We study the quantum-confined Stark effect in single InAs/GaAs quantum dots embedded within a AlGaAs/GaAs/AlGaAs quantum well. By significantly increasing the barrier height we can observe emission from a dot at electric fields of -500 kV/cm, leading to Stark shifts of up to 25 meV. Our results suggest this technique may enable future applications that require self-assembled dots with transitions at the same energy

Cavity-enhanced radiative emission rate in a single-photon-emitting diode operating at 0.5 GHz

We report the observation of a Purcell enhancement in the electroluminescence decay rate of a single quantum dot, embedded in a microcavity light-emitting-diode structure. Lateral confinement of the optical mode was achieved using an annulus of low-refractive-index aluminium oxide, formed by wet oxidation. The same layer acts as a current aperture, reducing the active area of the device without impeding the electrical properties of the p-i-n diode. This allowed single photon electroluminescence to be demonstrated at repetition rates up to 0.5 GHz.Comment: 11 pages, 4 Figures. To be published in New Journal of Physic

Bioinformatics characterization of BcsA-like orphan proteins suggest they form a novel family of pseudomonad cyclic-β-glucan synthases

Bacteria produce a variety of polysaccharides with functional roles in cell surface coating, surface and host interactions, and biofilms. We have identified an ‘Orphan’ bacterial cellulose synthase catalytic subunit (BcsA)-like protein found in four model pseudomonads, P. aeruginosa PA01, P. fluorescens SBW25, P. putida KT2440 and P. syringae pv. tomato DC3000. Pairwise alignments indicated that the Orphan and BcsA proteins shared less than 41% sequence identity suggesting they may not have the same structural folds or function. We identified 112 Orphans among soil and plant-associated pseudomonads as well as in phytopathogenic and human opportunistic pathogenic strains. The wide distribution of these highly conserved proteins suggest they form a novel family of synthases producing a different polysaccharide. In silico analysis, including sequence comparisons, secondary structure and topology predictions, and protein structural modelling, revealed a two-domain transmembrane ovoid-like structure for the Orphan protein with a periplasmic glycosyl hydrolase family GH17 domain linked via a transmembrane region to a cytoplasmic glycosyltransferase family GT2 domain. We suggest the GT2 domain synthesises β-(1,3)-glucan that is transferred to the GH17 domain where it is cleaved and cyclised to produce cyclic-β-(1,3)-glucan (CβG). Our structural models are consistent with enzymatic characterisation and recent molecular simulations of the PaPA01 and PpKT2440 GH17 domains. It also provides a functional explanation linking PaPAK and PaPA14 Orphan (also known as NdvB) transposon mutants with CβG production and biofilm-associated antibiotic resistance. Importantly, cyclic glucans are also involved in osmoregulation, plant infection and induced systemic suppression, and our findings suggest this novel family of CβG synthases may provide similar range of adaptive responses for pseudomonads.<br/

Potential for equation discovery with AI in the climate sciences

Climate change and Artificial Intelligence (AI) are both attracting great interest across society. There is also substantial interest in merging the two sciences, with evidence already that AI can identify earlier precursors to extreme weather events. There are a range of AI algorithms, and selection of the most appropriate one maximizes the amount of additional understanding extractable for any dataset. However, most AI algorithms are statistically based and even with careful splitting between data for training and testing, they arguably remain as emulators. Emulators may make unreliable predictions when driven by out-of-sample forcing and climate change is an example of this, requiring understanding responses to atmospheric Greenhouse Gas (GHG) concentrations that may be substantially higher than present or the recent past. Notable, though, is the emerging AI technique of “equation discovery”. AI-derived equations from data also does not automatically guarantee good performance for new forcing regimes. However, access to equations rather than a statistical emulator guides system understanding, as their variables and parameters often have a better interpretation. Better process knowledge enables judgements as to whether equations are trusted under extrapolation. For many climate system attributes, descriptive equations are not yet fully available or may be unreliable. This uncertainty is hindering the development of Earth System Models (ESMs) which remain the main tool for projections of large-scale environmental change as GHGs rise. Here, we make the case for using AI-driven equation discovery in climate research, given that its outputs are more interpretable in terms of processes. As ESMs are based around the numerical discretisation of equations that describe climate components, equation discovery from new datasets provides a format amenable to direct inclusion into such models where representation of environmental systems is missing. We present three illustrative examples of how AI-led equation discovery may advance future climate science research. These are generating new equations related to atmospheric convection, parameter derivation for existing equations of the terrestrial carbon cycle, and (additional to ESM improvement) the creation of simplified models of large-scale oceanic features to assess Tipping Point (TP) risks

The heme-hemopexin scavenging system is active in the brain, and associates with outcome after subarachnoid hemorrhage

Background and Purpose – Long-term outcome after subarachnoid hemorrhage (SAH) is potentially linked to cytotoxic heme. Free heme is bound by hemopexin (Hpx) and rapidly scavenged by CD91. We hypothesized that heme scavenging in the brain would be associated with outcome after haemorrhage. Methods - Using cerebrospinal fluid (CSF) and tissue from SAH patients and control individuals, the activity of the intracranial CD91-Hpx system was examined using enzyme-linked immunoassays, ultra-high performance liquid chromatography and immunohistochemistry. Results - In control individuals, CSF Hpx was mainly synthesized intrathecally. After SAH, CSF Hpx was high in one-third of cases, and these patients had a higher probability of delayed cerebral ischaemia and poorer neurological outcome. The intracranial CD91-Hpx system was active after SAH since CD91 positively correlated with iron deposition in brain tissue. Heme-Hpx uptake saturated rapidly after SAH, since bound heme accumulated early in the CSF. When the blood-brain barrier was compromised following SAH, serum Hpx level was lower, suggesting heme transfer to the circulation for peripheral CD91 scavenging. Conclusions - The CD91-heme-Hpx scavenging system is important after SAH and merits further study as a potential prognostic marker and therapeutic target

Tunable Indistinguishable Photons From Remote Quantum Dots

Single semiconductor quantum dots have been widely studied within devices that can apply an electric field. In the most common system, the low energy offset between the InGaAs quantum dot and the surrounding GaAs material limits the magnitude of field that can be applied to tens of kVcm^-1, before carriers tunnel out of the dot. The Stark shift experienced by the emission line is typically 1 meV. We report that by embedding the quantum dots in a quantum well heterostructure the vertical field that can be applied is increased by over an order of magnitude whilst preserving the narrow linewidths, high internal quantum efficiencies and familiar emission spectra. Individual dots can then be continuously tuned to the same energy allowing for two-photon interference between remote, independent, quantum dots

Do advanced mathematics skills predict success in biology and chemistry degrees?

The mathematical preparedness of science undergraduates has been a subject of debate for some time. This paper investigates the relationship between school mathematics attainment and degree outcomes in biology and chemistry across England, a much larger scale of analysis than has hitherto been reported in the literature. A unique dataset which links the National Pupil Database for England (NPD) and Higher Education Statistics Agency (HESA) data is used to track the educational trajectories of a national cohort of 16-year-olds through their school and degree programmes. Multilevel regression models indicate that students who completed advanced mathematics qualifications prior to their university study of biology and chemistry were no more likely to attain the best degree outcomes than those without advanced mathematics. The models do, however, suggest that success in advanced chemistry at school predicts outcomes in undergraduate biology and vice versa. There are important social background differences and the impact of the university attended is considerable. We discuss a range of possible explanations of these findings

Integration of Synthetic Aperture Radar Imagery and Derived Products into Severe Weather Disaster Response

No abstract availabl

Amyloid-β nanotubes are associated with prion protein-dependent synaptotoxicity

Growing evidence suggests water-soluble, non-fibrillar forms of amyloid-β protein (Aβ) have important roles in Alzheimer’s disease with toxicities mimicked by synthetic Aβ1–42. However, no defined toxic structures acting via specific receptors have been identified and roles of proposed receptors, such as prion protein (PrP), remain controversial. Here we quantify binding to PrP of Aβ1–42 after different durations of aggregation. We show PrP-binding and PrP-dependent inhibition of long-term potentiation (LTP) correlate with the presence of protofibrils. Globular oligomers bind less avidly to PrP and do not inhibit LTP, whereas fibrils inhibit LTP in a PrP-independent manner. That only certain transient Aβ assemblies cause PrP-dependent toxicity explains conflicting reports regarding the involvement of PrP in Aβ-induced impairments. We show that these protofibrils contain a defined nanotubular structure with a previously unidentified triple helical conformation. Blocking the formation of Aβ nanotubes or their interaction with PrP might have a role in treatment of Alzheimer’s disease