A superconductor to superfluid phase transition in liquid metallic hydrogen

Although hydrogen is the simplest of atoms, it does not form the simplest of solids or liquids. Quantum effects in these phases are considerable (a consequence of the light proton mass) and they have a demonstrable and often puzzling influence on many physical properties, including spatial order. To date, the structure of dense hydrogen remains experimentally elusive. Recent studies of the melting curve of hydrogen indicate that at high (but experimentally accessible) pressures, compressed hydrogen will adopt a liquid state, even at low temperatures. In reaching this phase, hydrogen is also projected to pass through an insulator-to-metal transition. This raises the possibility of new state of matter: a near ground-state liquid metal, and its ordered states in the quantum domain. Ordered quantum fluids are traditionally categorized as superconductors or superfluids; these respective systems feature dissipationless electrical currents or mass flow. Here we report an analysis based on topological arguments of the projected phase of liquid metallic hydrogen, finding that it may represent a new type of ordered quantum fluid. Specifically, we show that liquid metallic hydrogen cannot be categorized exclusively as a superconductor or superfluid. We predict that, in the presence of a magnetic field, liquid metallic hydrogen will exhibit several phase transitions to ordered states, ranging from superconductors to superfluids.Comment: for a related paper see cond-mat/0410425. A correction to the front page caption appeared in Oct 14 issue of Nature: http://www.nature.com/nature/links/041014/041014-11.htm

Paternal and maternal influences on differences in birth weight between Europeans and Indians born in the UK.

BACKGROUND: Ethnic groups differ significantly in adult physique and birth weight. We aimed to improve understanding of maternal versus paternal contributions to ethnic differences in birth weight, by comparing the offspring of same-ethnic versus mixed-ethnic unions amongst Europeans and South Asian Indians in the UK. METHODOLOGY AND PRINCIPAL FINDINGS: We used data from the UK Office for National Statistics Longitudinal Study (LS) and the Chelsea and Westminster Hospital (CWH), London. In the combined sample at all gestational ages, average birth weight of offspring with two European parents was significantly greater than that of offspring with two Indian parents [Δ = 344 (95% CI 329, 360) g]. Compared to offspring of European mothers, the offspring of Indian mothers had lower birth weight, whether the father was European [Δ = -152 (95% CI -92, -212) g] or Indian [Δ = -254 (95% -315, -192) g]. After adjustment for various confounding factors, average birth weight of offspring with European father and Indian mother was greater than that of offspring with two Indian parents [LS: Δ = 249 (95% CI 143, 354) g; CWH: Δ = 236 (95% CI 62, 411) g]. Average birth weight of offspring with Indian father and European mother was significantly less than that of offspring with two European parents [LS: Δ = -117 (95% CI -207, -26) g; CWH: Δ = -83 (-206, 40) g]. CONCLUSIONS/SIGNIFICANCE: Birth weight of offspring with mixed-ethnic parentage was intermediate between that of offspring with two European or two Indian parents, demonstrating a paternal as well as a maternal contribution to ethnic differences in fetal growth. This can be interpreted as demonstrating paternal modulation of maternal investment in offspring. We suggest long-term nutritional experience over generations may drive such ethnic differences through parental co-adaptation

Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning

Recent studies in the field of deep learning suggest that motion estimation can be treated as a learnable problem. In this paper we propose a pipeline for functional imaging in echocardiography consisting of four central components, (i) classification of cardiac view, (ii) semantic partitioning of the left ventricle (LV) myocardium, (iii) regional motion estimates and (iv) fusion of measurements. A U-Net type of convolutional neural network (CNN) was developed to classify muscle tissue, and partitioned into a semantic measurement kernel based on LV length and ventricular orientation. Dense tissue motion was predicted using stacked U-Net architectures with image warping of intermediate flow, designed to tackle variable displacements. Training was performed on a mixture of real and synthetic data. The resulting segmentation and motion estimates was fused in a Kalman filter and used as basis for measuring global longitudinal strain. For reference, 2D ultrasound images from 21 subjects were acquired using a GE Vivid system. Data was analyzed by two specialists using a semi-automatic tool for longitudinal function estimates in a commercial system, and further compared to output of the proposed method. Qualitative assessment showed comparable deformation trends as the clinical analysis software. The average deviation for the global longitudinal strain was (−0.6±1.6 −0.6±1.6 )% for apical four-chamber view. The system was implemented with Tensorflow, and working in an end-to-end fashion without any ad-hoc tuning. Using a modern graphics processing unit, the average inference time is estimated to (115±3 115±3 ) ms per frame.acceptedVersio