Quantum walks with memory provided by recycled coins and a memory of the coin-flip history

Quantum walks have emerged as an interesting approach to quantum information processing, exhibiting many unique properties compared to the analogous classical random walk. Here we introduce a model for a discrete-time quantum walk with memory by endowing the walker with multiple recycled coins and using a physical memory function via a history dependent coin flip. By numerical simulation we observe several phenomena. First in one dimension, walkers with memory have persistent quantum ballistic speed up over classical walks just as found in previous studies of multicoined walks with trivial memory function. However, measurement of the multicoin state can dramatically shift the mean of the spatial distribution. Second, we consider spatial entanglement in a two-dimensional quantum walk with memory and find that memory destroys entanglement between the spatial dimensions, even when entangling coins are employed. Finally, we explore behavior in the presence of spatial randomness and find that in the time regime where single-coined walks localize, multicoined walks do not and in fact a memory function can speed up the walk relative to a multicoin walker with no memory. We explicitly show how to construct linear optics circuits implementing the walks, and discuss prospects for classical simulation. © 2013 American Physical Society

Multiscale quantum simulation of quantum field theory using wavelets

© 2015 American Physical Society. ©2015 American Physical Society. A successful approach to understand field theories is to resolve the physics into different length or energy scales using the renormalization group framework. We propose a quantum simulation of quantum field theory which encodes field degrees of freedom in a wavelet basis - a multiscale description of the theory. Since wavelet families can be constructed to have compact support at all resolutions, this encoding allows for quantum simulations to create particle excitations which are local at some chosen scale and provides a natural way to associate observables in the theory to finite-resolution detectors

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operatordependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the ''ground truth''). After the expert defined the ground truth, the computer was able to develop a prediction rule that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1%±0.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5%± 0.04% in acinar area compared with the pathologist (p = 0.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas

Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays

BACKGROUND: Tissue microarrays (TMAs) have become a valuable resource for biomarker expression in translational research. Immunohistochemical (IHC) assessment of TMAs is the principal method for analysing large numbers of patient samples, but manual IHC assessment of TMAs remains a challenging and laborious task. With advances in image analysis, computer-generated analyses of TMAs have the potential to lessen the burden of expert pathologist review. METHODS: In current commercial software computerised oestrogen receptor (ER) scoring relies on tumour localisation in the form of hand-drawn annotations. In this study, tumour localisation for ER scoring was evaluated comparing computer-generated segmentation masks with those of two specialist breast pathologists. Automatically and manually obtained segmentation masks were used to obtain IHC scores for thirty-two ER-stained invasive breast cancer TMA samples using FDA-approved IHC scoring software. RESULTS: Although pixel-level comparisons showed lower agreement between automated and manual segmentation masks (κ=0.81) than between pathologists' masks (κ=0.91), this had little impact on computed IHC scores (Allred; [Image: see text]=0.91, Quickscore; [Image: see text]=0.92). CONCLUSIONS: The proposed automated system provides consistent measurements thus ensuring standardisation, and shows promise for increasing IHC analysis of nuclear staining in TMAs from large clinical trials

Pentraxin-3 as a Marker of Advanced Atherosclerosis Results from the Bruneck, ARMY and ARFY Studies

PubMed ID: 22319633This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Asymptotic equivalence of discretely observed diffusion processes and their Euler scheme: small variance case

This paper establishes the global asymptotic equivalence, in the sense of the Le Cam $\Delta$-distance, between scalar diffusion models with unknown drift function and small variance on the one side, and nonparametric autoregressive models on the other side. The time horizon $T$ is kept fixed and both the cases of discrete and continuous observation of the path are treated. We allow non constant diffusion coefficient, bounded but possibly tending to zero. The asymptotic equivalences are established by constructing explicit equivalence mappings.Comment: 21 page

Increased autophagy in EphrinB2-deficient osteocytes is associated with elevated secondary mineralization and brittle bone

Mineralized bone forms when collagen-containing osteoid accrues mineral crystals. This is initiated rapidly (primary mineralization), and continues slowly (secondary mineralization) until bone is remodeled. The interconnected osteocyte network within the bone matrix differentiates from bone-forming osteoblasts; although osteoblast differentiation requires EphrinB2, osteocytes retain its expression. Here we report brittle bones in mice with osteocyte-targeted EphrinB2 deletion. This is not caused by low bone mass, but by defective bone material. While osteoid mineralization is initiated at normal rate, mineral accrual is accelerated, indicating that EphrinB2 in osteocytes limits mineral accumulation. No known regulators of mineralization are modified in the brittle cortical bone but a cluster of autophagy-associated genes are dysregulated. EphrinB2-deficient osteocytes displayed more autophagosomes in vivo and in vitro, and EphrinB2-Fc treatment suppresses autophagy in a RhoA-ROCK dependent manner. We conclude that secondary mineralization involves EphrinB2-RhoA-limited autophagy in osteocytes, and disruption leads to a bone fragility independent of bone mass

Lack of the Delta Subunit of RNA Polymerase Increases Virulence Related Traits of Streptococcus mutans

The delta subunit of the RNA polymerase, RpoE, maintains the transcriptional specificity in Gram-positive bacteria. Lack of RpoE results in massive changes in the transcriptome of the human dental caries pathogen Streptococcus mutans. In this study, we analyzed traits of the ΔrpoE mutant which are important for biofilm formation and interaction with oral microorganisms and human cells and performed a global phenotypic analysis of its physiological functions. The ΔrpoE mutant showed higher self-aggregation compared to the wild type and coaggregated with other oral bacteria and Candida albicans. It formed a biofilm with a different matrix structure and an altered surface attachment. The amount of the cell surface antigens I/II SpaP and the glucosyltransferase GtfB was reduced. The ΔrpoE mutant displayed significantly stronger adhesion to human extracellular matrix components, especially to fibronectin, than the wild type. Its adhesion to human epithelial cells HEp-2 was reduced, probably due to the highly aggregated cell mass. The analysis of 1248 physiological traits using phenotype microarrays showed that the ΔrpoE mutant metabolized a wider spectrum of carbon sources than the wild type and had acquired resistance to antibiotics and inhibitory compounds with various modes of action. The reduced antigenicity, increased aggregation, adherence to fibronection, broader substrate spectrum and increased resistance to antibiotics of the ΔrpoE mutant reveal the physiological potential of S. mutans and show that some of its virulence related traits are increased