Appearance of the Single Gyroid Network Phase in Nuclear Pasta Matter

Nuclear matter under the conditions of a supernova explosion unfolds into a rich variety of spatially structured phases, called nuclear pasta. We investigate the role of periodic network-like structures with negatively curved interfaces in nuclear pasta structures, by static and dynamic Hartree-Fock simulations in periodic lattices. As the most prominent result, we identify for the first time the {\it single gyroid} network structure of cubic chiral $I4_123$ symmetry, a well known configuration in nanostructured soft-matter systems, both as a dynamical state and as a cooled static solution. Single gyroid structures form spontaneously in the course of the dynamical simulations. Most of them are isomeric states. The very small energy differences to the ground state indicate its relevance for structures in nuclear pasta.Comment: 7 pages, 4 figure

Covariation Among Vowel Height Effects on Acoustic Measures

Covariation among vowel height effects on vowel intrinsic fundamental frequency (IF0), voice onset time (VOT), and voiceless interval duration (VID) is analyzed to assess the plausibility of a common physiological mechanism underlying variation in these measures. Phrases spoken by 20 young adults, containing words composed of initial voiceless stops or /s/ and high or low vowels, were produced in habitual and voluntarily increased F0 conditions. High vowels were associated with increased IF0 and longer VIDs. VOT and VID exhibited significant covariation with IF0 only for males at habitua

Association of Systemic Inflammation With Retinal Vascular Caliber in Patients With AIDS.

PurposeTo evaluate relationships among retinal vascular caliber and biomarkers of systemic inflammation in patients with AIDS.MethodsA total of 454 participants with AIDS had retinal vascular caliber (central retinal artery equivalent and central retinal vein equivalent) determined from enrollment retinal photographs by reading center graders masked to clinical and biomarker information. Cryopreserved plasma specimens were assayed for inflammatory biomarkers, including C-reactive protein (CRP), IL-6, interferon-γ inducible protein (IP)-10, kynurenine/tryptophan (KT) ratio, and intestinal fatty acid binding protein (I-FABP).ResultsIn the simple linear regression of retinal vascular caliber on plasma biomarkers, elevated CRP, IL-6, and IP-10 were associated with retinal venular dilation, and elevated KT ratio with retinal arteriolar narrowing. In the multiple linear regression, including baseline characteristics and plasma biomarkers, AMD was associated with dilation of retinal arterioles (mean difference: 9.1 μm; 95% confidence interval [CI] 5.2, 12.9; P < 0.001) and venules (mean difference, 10.9 μm; 95% CI, 5.3, 16.6; P < 0.001), as was black race (P < 0.001). Hyperlipidemia was associated with retinal venular narrowing (mean difference, -7.5 μm; 95% CI, -13.7, -1.2; P = 0.02); cardiovascular disease with arteriolar narrowing (mean difference, -5.2 μm; 95% CI, -10.3, -0.1; P = 0.05); age with arteriolar narrowing (slope, -0.26 μm/year; 95% CI, -0.46, -0.06; P = 0.009); and IL-6 with venular dilation (slope, 5.3 μm/standard deviation log10[plasma IL-6 concentration]; 95% CI, 2.7, 8.0; P < 0.001).ConclusionsThese data suggest that retinal vascular caliber is associated with age, race, AMD, hyperlipidemia, cardiovascular disease, and selected biomarkers of systemic inflammation

Minimal surfaces in nuclear pasta with the Time-Dependent Hartree-Fock approach

In continuation to the studies of the whole variety of pasta shapes in [1], we present here calculations performed with the Hartree-Fock and time-dependent Hartree- Fock method concerning the mid-density range of pasta shapes: The slab-like, connected rod-like (p-surface) and the gyroidal shapes. On the one hand we present simulations of the dynamic formation of these shapes at fi- nite temperature. On the other hand we calculate the binding energies of these shapes for varying simulation box lengths and mean densities. All of these shapes are found to be at least metastable. The slab shape has a slightly lower energy because of the lack of curvature, but among these three configurations the gyroidal shape is metastable for the widest range in mean density

Pollinators, pests and yield-Multiple trade-offs from insecticide use in a mass-flowering crop

Multiple trade-offs likely occur between pesticide use, pollinators and yield (via crop flowers) in pollinator-dependent, mass-flowering crops (MFCs), causing potential conflict between conservation and agronomic goals. To date, no studies have looked at both outcomes within the same system, meaning win-win solutions for pollinators and yield can only be inferred. Here, we outline a new framework to explore these trade-offs, using red clover (Trifolium pratense) grown for seed production as an example. Specifically, we address how the insecticide thiacloprid affects densities of seed-eating weevils (Protapion spp.), pollination rates, yield, floral resources and colony dynamics of the key pollinator, Bombus terrestris. Thiacloprid did not affect the amount of nectar provided by, or pollinator visitation to, red clover flowers but did reduce weevil density, correlating to increased yield and gross profit. In addition, colonies of B. terrestris significantly increased their weight and reproductive output in landscapes with (compared with without) red clover, regardless of insecticide use. Synthesis and applications. We propose a holistic conceptual framework to explore trade-offs between pollinators, pesticides and yield that we believe to be essential for achieving conservation and agronomic goals. This framework applies to all insecticide-treated mass-flowering crops (MFCs) and can be adapted to include other ecological processes. Trialling the framework in our study system, we found that our focal insecticide, thiacloprid, improved red clover seed yield with no detected effects on its key pollinator, B. terrestris, and that the presence of red clover in the landscape can benefit pollinator populations

Deep-pretrained-FWI: combining supervised learning with physics-informed neural network

An accurate velocity model is essential to make a good seismic image. Conventional methods to perform Velocity Model Building (VMB) tasks rely on inverse methods, which, despite being widely used, are ill-posed problems that require intense and specialized human supervision. Convolutional Neural Networks (CNN) have been extensively investigated as an alternative to solve the VMB task. Two main approaches were investigated in the literature: supervised training and Physics-Informed Neural Networks (PINN). Supervised training presents some generalization issues since structures, and velocity ranges must be similar in training and test set. Some works integrated Full-waveform Inversion (FWI) with CNN, defining the problem of VMB in the PINN framework. In this case, the CNN stabilizes the inversion, acting like a regularizer and avoiding local minima-related problems and, in some cases, sparing an initial velocity model. Our approach combines supervised and physics-informed neural networks by using transfer learning to start the inversion. The pre-trained CNN is obtained using a supervised approach based on training with a reduced and simple data set to capture the main velocity trend at the initial FWI iterations. We show that transfer learning reduces the uncertainties of the process, accelerates model convergence, and improves the final scores of the iterative process.Comment: Paper present at machine Learning and the Physical Sciences workshop, NeurIPS 202