Finite temperature stability and dimensional crossover of exotic superfluidity in lattices

We investigate exotic paired states of spin-imbalanced Fermi gases in anisotropic lattices, tuning the dimension between one and three. We calculate the finite temperature phase diagram of the system using real-space dynamical mean-field theory in combination with the quantum Monte Carlo method. We find that regardless of the intermediate dimensions examined, the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state survives to reach about one third of the BCS critical temperature of the spin-density balanced case. We show how the gapless nature of the state found is reflected in the local spectral function. While the FFLO state is found at a wide range of polarizations at low temperatures across the dimensional crossover, with increasing temperature we find out strongly dimensionality-dependent melting characteristics of shell structures related to harmonic confinement. Moreover, we show that intermediate dimension can help to stabilize an extremely uniform finite temperature FFLO state despite the presence of harmonic confinement.Comment: 5 pages, 3 figure

Right Ventricle Has Normal Myofilament Function But Shows Perturbations in the Expression of Extracellular Matrix Genes in Patients With Tetralogy of Fallot Undergoing Pulmonary Valve Replacement

BACKGROUND: Patients with repair of tetralogy of Fallot (rToF) who are approaching adulthood often exhibit pulmonary valve regurgitation, leading to right ventricle (RV) dilatation and dysfunction. The regurgitation can be corrected by pulmonary valve replacement (PVR), but the optimal surgical timing remains under debate, mainly because of the poorly understood nature of RV remodeling in patients with rToF. The goal of this study was to probe for pathologic molecular, cellular, and tissue changes in the myocardium of patients with rToF at the time of PVR. METHODS AND RESULTS: We measured contractile function of permeabilized myocytes, collagen content of tissue samples, and the expression of mRNA and selected proteins in RV tissue samples from patients with rToF undergoing PVR for severe pulmonary valve regurgitation. The data were compared with nondiseased RV tissue from unused donor hearts. Contractile performance and passive stiffness of the myofilaments in permeabilized myocytes were similar in rToF‐PVR and RV donor samples, as was collagen content and cross‐linking. The patients with rToF undergoing PVR had enhanced mRNA expression of genes associated with connective tissue diseases and tissue remodeling, including the small leucine‐rich proteoglycans ASPN (asporin), LUM (lumican), and OGN (osteoglycin), although their protein levels were not significantly increased. CONCLUSIONS: RV myofilaments from patients with rToF undergoing PVR showed no functional impairment, but the changes in extracellular matrix gene expression may indicate the early stages of remodeling. Our study found no evidence of major damage at the cellular and tissue levels in the RV of patients with rToF who underwent PVR according to current clinical criteria

Collective Excitations of Holographic Quantum Liquids in a Magnetic Field

We use holography to study N=4 supersymmetric SU(Nc) Yang-Mills theory in the large-Nc and large-coupling limits coupled to a number Nf << Nc of (n+1)-dimensional massless supersymmetric hypermultiplets in the Nc representation of SU(Nc), with n=2,3. We introduce a temperature T, a baryon number chemical potential mu, and a baryon number magnetic field B, and work in a regime with mu >> T,\sqrt{B}. We study the collective excitations of these holographic quantum liquids by computing the poles in the retarded Green's function of the baryon number charge density operator and the associated peaks in the spectral function. We focus on the evolution of the collective excitations as we increase the frequency relative to T, i.e. the hydrodynamic/collisionless crossover. We find that for all B, at low frequencies the tallest peak in the spectral function is associated with hydrodynamic charge diffusion. At high frequencies the tallest peak is associated with a sound mode similar to the zero sound mode in the collisionless regime of a Landau Fermi liquid. The sound mode has a gap proportional to B, and as a result for intermediate frequencies and for B sufficiently large compared to T the spectral function is strongly suppressed. We find that the hydrodynamic/collisionless crossover occurs at a frequency that is approximately B-independent.Comment: 45 pages, 8 png and 47 pdf images in 22 figure

Quantitative ultrasound tissue characterization in shoulder and thigh muscles – a new approach

BACKGROUND: The echogenicity patterns of ultrasound scans contain information of tissue composition in muscles. The aim was: (1) to develop a quantitative ultrasound image analysis to characterize tissue composition in terms of intensity and structure of the ultrasound images, and (2) to use the method for characterization of ultrasound images of the supraspinatus muscle, and the vastus lateralis muscle. METHODS: Computerized texture analyses employing first-order and higher-order grey-scale statistics were developed to objectively characterize ultrasound images of m. supraspinatus and m. vastus lateralis from 9 healthy participants. RESULTS: The mean grey-scale intensity was higher in the vastus lateralis muscle (p < 0.05) than in the supraspinatus muscle (average value of middle measuring site 51.4 compared to 35.0). Furthermore, the number of spatially connected and homogeneous regions (blobs) was higher in the vastus lateralis (p < 0.05) than in the supraspinatus (average for m. vastus lateralis: 0.092 mm(-2 )and for m. supraspinatus: 0.016 mm(-2)). CONCLUSION: The higher intensity and the higher number of blobs in the vastus lateralis muscle indicates that the thigh muscle contained more non-contractile components than the supraspinatus muscle, and that the muscle was coarser. The image analyses supplemented each other and gave a more complete description of the tissue composition in the muscle than the mean grey-scale value alone

Classical kinetic energy, quantum fluctuation terms and kinetic-energy functionals

We employ a recently formulated dequantization procedure to obtain an exact expression for the kinetic energy which is applicable to all kinetic-energy functionals. We express the kinetic energy of an N-electron system as the sum of an N-electron classical kinetic energy and an N-electron purely quantum kinetic energy arising from the quantum fluctuations that turn the classical momentum into the quantum momentum. This leads to an interesting analogy with Nelson's stochastic approach to quantum mechanics, which we use to conceptually clarify the physical nature of part of the kinetic-energy functional in terms of statistical fluctuations and in direct correspondence with Fisher Information Theory. We show that the N-electron purely quantum kinetic energy can be written as the sum of the (one-electron) Weizsacker term and an (N-1)-electron kinetic correlation term. We further show that the Weizsacker term results from local fluctuations while the kinetic correlation term results from the nonlocal fluctuations. For one-electron orbitals (where kinetic correlation is neglected) we obtain an exact (albeit impractical) expression for the noninteracting kinetic energy as the sum of the classical kinetic energy and the Weizsacker term. The classical kinetic energy is seen to be explicitly dependent on the electron phase and this has implications for the development of accurate orbital-free kinetic-energy functionals. Also, there is a direct connection between the classical kinetic energy and the angular momentum and, across a row of the periodic table, the classical kinetic energy component of the noninteracting kinetic energy generally increases as Z increases.Comment: 10 pages, 1 figure. To appear in Theor Chem Ac

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Characterization of the L-Lactate Dehydrogenase from Aggregatibacter actinomycetemcomitans

Aggregatibacter actinomycetemcomitans is a Gram-negative opportunistic pathogen and the proposed causative agent of localized aggressive periodontitis. A. actinomycetemcomitans is found exclusively in the mammalian oral cavity in the space between the gums and the teeth known as the gingival crevice. Many bacterial species reside in this environment where competition for carbon is high. A. actinomycetemcomitans utilizes a unique carbon resource partitioning system whereby the presence of L-lactate inhibits uptake of glucose, thus allowing preferential catabolism of L-lactate. Although the mechanism for this process is not fully elucidated, we previously demonstrated that high levels of intracellular pyruvate are critical for L-lactate preference. As the first step in L-lactate catabolism is conversion of L-lactate to pyruvate by lactate dehydrogenase, we proposed a model in which the A. actinomycetemcomitans L-lactate dehydrogenase, unlike homologous enzymes, is not feedback inhibited by pyruvate. This lack of feedback inhibition allows intracellular pyruvate to rise to levels sufficient to inhibit glucose uptake in other bacteria. In the present study, the A. actinomycetemcomitans L-lactate dehydrogenase was purified and shown to convert L-lactate, but not D-lactate, to pyruvate with a Km of approximately 150 µM. Inhibition studies reveal that pyruvate is a poor inhibitor of L-lactate dehydrogenase activity, providing mechanistic insight into L-lactate preference in A. actinomycetemcomitans