Ion-scale spectral break of solar wind turbulence at high and low beta

The power spectrum of magnetic fluctuations in the solar wind at 1 AU displays a break between two power laws in the range of spacecraft-frame frequencies 0.1 to 1 Hz. These frequencies correspond to spatial scales in the plasma frame near the proton gyroradius ρi and proton inertial length di. At 1 AU it is difficult to determine which of these is associated with the break, since [Formula: see text] and the perpendicular ion plasma beta is typically β⊥i∼1. To address this, several exceptional intervals with β⊥i≪1 and β⊥i≫1 were investigated, during which these scales were well separated. It was found that for β⊥i≪1 the break occurs at di and for β⊥i≫1 at ρi, i.e., the larger of the two scales. Possible explanations for these results are discussed, including Alfvén wave dispersion, damping, and current sheets

Temperature dependent dynamics of photoexcited carriers of Si2Te3 nanowires

We report an optical study of the dynamics of photoexcited carriers in Si2Te3 nanowires at various temperatures and excitation powers. Si2Te3 nanowires were synthesized, by using gold as a catalyst, on a silicon substrate by the chemical vapor deposition method. The photoluminescence spectrum of Si2Te3 nanowires was primary dominated by defect and surface states related emission at both low and room temperatures. We observed that the decay time of photoexcited carries was very long (> 10 ns) at low temperatures and became shorter (< 2 ns) at room temperature. Further, the carrier decay time became faster at high excitation rates. The acceleration of the photoexcited carrier decay rates indicate the thermal quenching along with the non-radiative recombination at high temperature and excitation power. Our results have quantitatively elucidated decay mechanisms that are important towards understanding and controlling of the electronic states in Si2Te3 nanostructures for optoelectronic applications.Comment: 12 pages, 4 figures, submitte

Fiber Orientation Estimation Guided by a Deep Network

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs with a relatively small number of diffusion gradients. However, accurate FO estimation in regions with complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent the diffusion signals. To estimate the mixture fractions of the dictionary atoms (and thus coarse FOs), a deep network is designed specifically for solving the sparse reconstruction problem. Here, the smaller dictionary is used to reduce the computational cost of training. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding dense basis FOs is used and a weighted l1-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and real dMRI data, and the results demonstrate the benefit of using a deep network for FO estimation.Comment: A shorter version is accepted by MICCAI 201

Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images

Thurnhofer-Hemsi K., López-Rubio E., Roé-Vellvé N., Molina-Cabello M.A. (2019) Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer, ChamNowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the min- imization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust t. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Central and peripheral circadian clocks and their role in Alzheimer's disease

Molecular and cellular oscillations constitute an internal clock that tracks the time of day and permits organisms to optimize their behaviour and metabolism to suit the daily demands they face. The workings of this internal clock become impaired with age. In this review, we discuss whether such age-related impairments in the circadian clock interact with age-related neurodegenerative disorders, such as Alzheimer's disease. Findings from mouse and fly models of Alzheimer's disease have accelerated our understanding of the interaction between neurodegeneration and circadian biology. These models show that neurodegeneration likely impairs circadian rhythms either by damaging the central clock or by blocking its communication with other brain areas and with peripheral tissues. The consequent sleep and metabolic deficits could enhance the susceptibility of the brain to further degenerative processes. Thus, circadian dysfunction might be both a cause and an effect of neurodegeneration. We also discuss the primary role of light in the entrainment of the central clock and describe important, alternative time signals, such as food, that play a role in entraining central and peripheral circadian clocks. Finally, we propose how these recent insights could inform efforts to develop novel therapeutic approaches to re-entrain arrhythmic individuals with neurodegenerative disease

Unsaturated phosphatidylcholines lining on the surface of cartilage and its possible physiological roles

Background Evidence has strongly indicated that surface-active phospholipid (SAPL), or surfactant, lines the surface of cartilage and serves as a lubricating agent. Previous clinical study showed that a saturated phosphatidylcholine (SPC), dipalmitoyl-phosphatidylcholine (DPPC), was effective in the treatment of osteoarthritis, however recent studies suggested that the dominant SAPL species at some sites outside the lung are not SPC, rather, are unsaturated phosphatidylcholine (USPC). Some of these USPC have been proven to be good boundary lubricants by our previous study, implicating their possible important physiological roles in joint if their existence can be confirmed. So far, no study has been conducted to identify the whole molecule species of different phosphatidylcholine (PC) classes on the surface of cartilage. In this study we identified the dominant PC molecule species on the surface of cartilage. We also confirmed that some of these PC species possess a property of semipermeability. Methods HPLC was used to analyse the PC profile of bovine cartilage samples and comparisons of DPPC and USPC were carried out through semipermeability tests. Results It was confirmed that USPC are the dominant SAPL species on the surface of cartilage. In particular, they are Dilinoleoyl-phosphatidylcholine (DLPC), Palmitoyl-linoleoyl-phosphatidylcholine, (PLPC), Palmitoyl-oleoyl-phosphatidylcholine (POPC) and Stearoyl-linoleoyl-phosphatidylcholine (SLPC). The relative content of DPPC (a SPC) was only 8%. Two USPC, PLPC and POPC, were capable of generating osmotic pressure that is equivalent to that by DPPC. Conclusion The results from the current study confirm vigorously that USPC is the endogenous species inside the joint as against DPPC thereby confirming once again that USPC, and not SPC, characterizes the PC species distribution at non-lung sites of the body. USPC not only has better anti-friction and lubrication properties than DPPC, they also possess a level of semipermeability that is equivalent to DPPC. We therefore hypothesize that USPC can constitute a possible addition or alternative to the current commercially available viscosupplementation products for the prevention and treatment of osteoarthritis in the future

A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction

Ground settlement prediction during the process of mechanized tunneling is of paramount importance and remains a challenging research topic. Typically, two paradigms are existing: a physics-driven approach utilizing process-oriented computational simulation models for the tunnel-soil interaction and the settlement prediction, and a data-driven approach employing machine learning techniques to establish mappings between influencing factors and the ground settlement. To integrate the advantages of both approaches and to assimilate the data from different sources, we propose a multi-fidelity deep operator network (DeepONet) framework, leveraging the recently developed operator learning methods. The presented framework comprises of two components: a low-fidelity subnet that captures the fundamental ground settlement patterns obtained from finite element simulations, and a high-fidelity subnet that learns the nonlinear correlation between numerical models and real engineering monitoring data. A pre-processing strategy for causality is adopted to consider the spatio-temporal characteristics of the settlement during tunnel excavation. Transfer learning is utilized to reduce the training cost for the low-fidelity subnet. The results show that the proposed method can effectively capture the physical information provided by the numerical simulations and accurately fit measured data as well. Remarkably, even with very limited noisy monitoring data, the proposed model can achieve rapid, accurate, and robust predictions of the full-field ground settlement in real-time during mechanized tunnel excavation

Tunability and Robustness of Dirac Points of Photonic Nanostructures

We study the tunability and robustness of photonic Dirac points (DPs) in plasmonic nanostructures. The tunability of the DP is demonstrated in graphene-based photonic superlattices by adjusting the graphene permittivity via the optical Kerr effect or electrical doping. The robustness of DPs is demonstrated in plasmonic lattices by showing that even very high levels of disorder are unable to localize the modes located near the DP. The robustness of the DP also manifests itself in the fact that the inversely-proportional dependence of the transmission on the lattice length near the DP remains unchanged under strong disorder