Entanglement between Collective Operators in a Linear Harmonic Chain

We investigate entanglement between collective operators of two blocks of oscillators in an infinite linear harmonic chain. These operators are defined as averages over local operators (individual oscillators) in the blocks. On the one hand, this approach of "physical blocks" meets realistic experimental conditions, where measurement apparatuses do not interact with single oscillators but rather with a whole bunch of them, i.e., where in contrast to usually studied "mathematical blocks" not every possible measurement is allowed. On the other, this formalism naturally allows the generalization to blocks which may consist of several non-contiguous regions. We quantify entanglement between the collective operators by a measure based on the Peres-Horodecki criterion and show how it can be extracted and transferred to two qubits. Entanglement between two blocks is found even in the case where none of the oscillators from one block is entangled with an oscillator from the other, showing genuine bipartite entanglement between collective operators. Allowing the blocks to consist of a periodic sequence of subblocks, we verify that entanglement scales at most with the total boundary region. We also apply the approach of collective operators to scalar quantum field theory.Comment: 7 pages, 4 figures, significantly revised version with new results, journal reference adde

The late flowering of invasive species contributes to the increase of Artemisia allergenic pollen in autumn: an analysis of 25 years of aerobiological data (1995–2019) in Trentino-Alto Adige (Northern Italy)

Artemisia pollen is an important aeroallergen in late summer, especially in central and eastern Europe where distinct anemophilous Artemisia spp. produce high amounts of pollen grains. The study aims at: (i) analyzing the temporal pattern of and changes in the Artemisia spp. pollen season; (ii) identifying the Artemisia species responsible for the local airborne pollen load. Daily pollen concentration of Artemisia spp. was analyzed at two sites (BZ and SM) in Trentino-Alto Adige, North Italy, from 1995 to 2019. The analysis of airborne Artemisia pollen concentrations evidences the presence of a bimodal curve, with two peaks, in August and September, respectively. The magnitude of peak concentrations varies across the studied time span for both sites: the maximum concentration at the September peak increases significantly for both the BZ (p < 0.05) and SM (p < 0.001) site. The first peak in the pollen calendar is attributable to native Artemisia species, with A. vulgaris as the most abundant; the second peak is mostly represented by the invasive species A. annua and A. verlotiorum (in constant proportion along the years), which are causing a considerable increase in pollen concentration in the late pollen season in recent years.. The spread of these species can affect human health, increasing the length and severity of allergenic pollen exposure in autumn, as well as plant biodiversity in both natural and cultivated areas, with negative impacts on, e.g., Natura 2000 protected sites and crops

An Extremely Rare Congenital Muscle Bundle Crossing the Right Atrial Cavity

Muscle bundles in the right atrium are an extremely rare congenital anomaly. We report the case of a patient with 2 atrial septal defects and a large muscle bundle crossing the right atrium. Only 3 comparable cases have previously been published. (Level of Difficulty: Intermediate.)

Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery

Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling

We introduce a method for the fast estimation of data-adapted, spatially and temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variation (TV) minimization. The proposed approach is inspired by recent developments in algorithm unrolling using deep neural networks (NNs) and relies on two distinct subnetworks. The first subnetwork estimates the regularization parameter-map from the input data. The second subnetwork unrolls iterations of an iterative algorithm which approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean and corrupted data but crucially without the need for access to labels for the optimal regularization parameter-maps. We first prove consistency of the unrolled scheme by showing that the unrolled minimizing energy functional used for the supervised learning -converges, as tends to infinity, to the corresponding functional that incorporates the exact solution map of the TV-minimization problem. Then, we apply and evaluate the proposed method on a variety of large-scale and dynamic imaging problems with retrospectively simulated measurement data for which the automatic computation of such regularization parameters has been so far challenging using the state-of-the-art methods: a 2D dynamic cardiac magnetic resonance imaging (MRI) reconstruction problem, a quantitative brain MRI reconstruction problem, a low-dose computed tomography problem, and a dynamic image denoising problem. The proposed method consistently improves the TV reconstructions using scalar regularization parameters, and the obtained regularization parameter-maps adapt well to imaging problems and data by leading to the preservation of detailed features. Although the choice of the regularization parameter-maps is data-driven and based on NNs, the subsequent reconstruction algorithm is interpretable since it inherits the properties (e.g., convergence guarantees) of the iterative reconstruction method from which the network is implicitly defined

Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction

We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based on two distinct sub-networks, with the first sub-network estimating the regularization parameter-map from the input data while the second one unrolling T iterations of the Primal-Dual Three-Operator Splitting (PD3O) algorithm. The latter approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean-corrupted data but crucially without the need of having access to labels for the optimal regularization parameter-maps

Quantum Histories and Quantum Gravity

This paper reviews the histories approach to quantum mechanics. This discussion is then applied to theories of quantum gravity. It is argued that some of the quantum histories must approximate (in a suitable sense) to classical histories, if the correct classical regime is to be recovered. This observation has significance for the formulation of new theories (such as quantum gravity theories) as it puts a constraint on the kinematics, if the quantum/classical correspondence principle is to be preserved. Consequences for quantum gravity, particularly for Lorentz symmetry and the idea of "emergent geometry", are discussed.Comment: 35 pages (29 pages main body), two figure

Profibrotic epithelial phenotype:a central role for MRTF and TAZ

Abstract Epithelial injury is a key initiator of fibrosis but - in contrast to the previous paradigm - the epithelium in situ does not undergo wide-spread epithelial-mesenchymal/myofibroblast transition (EMT/EMyT). Instead, it assumes a Profibrotic Epithelial Phenotype (PEP) characterized by fibrogenic cytokine production. The transcriptional mechanisms underlying PEP are undefined. As we have shown that two RhoA/cytoskeleton-regulated transcriptional coactivators, Myocardin-related transcription factor (MRTF) and TAZ, are indispensable for EMyT, we asked if they might mediate PEP as well. Here we show that mechanical stress (cyclic stretch) increased the expression of transforming growth factor-β1 (TGFβ1), connective tissue growth factor (CTGF), platelet-derived growth factor and Indian Hedgehog mRNA in LLC-PK1 tubular cells. These responses were mitigated by siRNA-mediated silencing or pharmacological inhibition of MRTF (CCG-1423) or TAZ (verteporfin). RhoA inhibition exerted similar effects. Unilateral ureteral obstruction, a murine model of mechanically-triggered kidney fibrosis, induced tubular RhoA activation along with overexpression/nuclear accumulation of MRTF and TAZ, and increased transcription of the above-mentioned cytokines. Laser capture microdissection revealed TAZ, TGFβ1 and CTGF induction specifically in the tubular epithelium. CCG-1423 suppressed total renal and tubular expression of these proteins. Thus, MRTF regulates epithelial TAZ expression, and both MRTF and TAZ are critical mediators of PEP-related epithelial cytokine production