A Novel Tree Shrew Model of Diabetic Retinopathy

Existing animal models with rod-dominant retinas have shown that hyperglycemia injures neurons, but it is not yet clearly understood how blue cone photoreceptors and retinal ganglion cells (RGCs) deteriorate in patients because of compromised insulin tolerance. In contrast, northern tree shrews (Tupaia Belangeri), one of the closest living relatives of primates, have a cone-dominant retina with short wave sensitivity (SWS) and long wave sensitivity (LWS) cones. Therefore, we injected animals with a single streptozotocin dose (175 mg/kg i.p.) to investigate whether sustained hyperglycemia models the features of human diabetic retinopathy (DR). We used the photopic electroretinogram (ERG) to measure the amplitudes of A and B waves and the photopic negative responses (PhNR) to evaluate cone and RGC function. Retinal flat mounts were prepared for immunohistochemical analysis to count the numbers of neurons with antibodies against cone opsins and RGC specific BRN3a proteins. The levels of the proteins TRIB3, ISR-1, and p-AKT/p-mTOR were measured with western blot. The results demonstrated that tree shrews manifested sustained hyperglycemia leading to a slight but significant loss of SWS cones (12%) and RGCs (20%) 16 weeks after streptozotocin injection. The loss of BRN3a-positive RGCs was also reflected by a 30% decline in BRN3a protein expression. These were accompanied by reduced ERG amplitudes and PhNRs. Importantly, the diabetic retinas demonstrated increased expression of TRIB3 and level of p-AKT/p-mTOR axis but reduced level of IRS-1 protein. Therefore, a new non-primate model of DR with SWS cone and RGC dysfunction lays the foundation to better understand retinal pathophysiology at the molecular level and opens an avenue for improving the research on the treatment of human eye diseases.

Short-wavelength Reverberant Wave Systems for Enhanced Reservoir Computing

Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed execution, physical robustness, and low energy cost. Here, we experimentally demonstrate an approach that uses the high sensitivity of reverberant short wavelength waves for physical realization and enhancement of computational power of a type of ML known as reservoir computing (RC). The potential computation power of RC systems increases with their effective size. We here exploit the intrinsic property of short wavelength reverberant wave sensitivity to perturbations to expand the effective size of the RC system by means of spatial and spectral perturbations. Working in the microwave regime, this scheme is tested on different ML tasks. Our results indicate the general applicability of reverberant wave-based implementations of RC and of our effective reservoir size expansion techniques.

A Novel Principle to Localize Scattered-wave Sensitivity Using Wave Interference and Its Adjoint

When using scattered waves for high-resolution imaging of a medium, the sensitivity of these waves to the spatiotemporal distribution of heterogeneities is undoubtedly a key factor. The traditional principle behind using scattered waves to detect small changes suffers from an inherent limitation when other structures, not of interest, are present along the wave propagation path. We propose a novel principle that leads to enhanced localization of wave sensitivity, without having to know the intermediate structures. This new principle emerges from a boundary integral representation which utilizes wave interferences observed at multiple points. When tested on geophysical acoustic wave data, this new principle leads to much better sensitivity localization and detection of small changes in seismic velocities, which were otherwise impossible. Overcoming the insensitivity to a target area, it offers new possibilities for imaging and monitoring small changes in properties, which is critical in a wide range of disciplines and scales.

Full wave sensitivity of SK(K)S phases to arbitrary anisotropy in the upper and lower mantle

SUMMARY Core-refracted phases such as SKS and SKKS are commonly used to probe seismic anisotropy in the upper and lowermost portions of the Earth’s mantle. Measurements of SK(K)S splitting are often interpreted in the context of ray theory, and their frequency dependent sensitivity to anisotropy remains imperfectly understood, particularly for anisotropy in the lowermost mantle. The goal of this work is to obtain constraints on the frequency dependent sensitivity of SK(K)S phases to mantle anisotropy, particularly at the base of the mantle, through global wavefield simulations. We present results from a new numerical approach to modelling the effects of seismic anisotropy of arbitrary geometry on seismic wave propagation in global 3-D earth models using the spectral element solver AxiSEM3D. While previous versions of AxiSEM3D were capable of handling radially anisotropic input models, here we take advantage of the ability of the solver to handle the full fourth-order elasticity tensor, with 21 independent coefficients. We take advantage of the computational efficiency of the method to compute wavefields at the relatively short periods (5 s) that are needed to simulate SK(K)S phases. We benchmark the code for simple, single-layer anisotropic models by measuring the splitting (via both the splitting intensity and the traditional splitting parameters ϕ and δt) of synthetic waveforms and comparing them to well-understood analytical solutions. We then carry out a series of numerical experiments for laterally homogeneous upper mantle anisotropic models with different symmetry classes, and compare the splitting of synthetic waveforms to predictions from ray theory. We next investigate the full wave sensitivity of SK(K)S phases to lowermost mantle anisotropy, using elasticity models based on crystallographic preferred orientation of bridgmanite and post-perovskite. We find that SK(K)S phases have significant sensitivity to anisotropy at the base of the mantle, and while ray theoretical approximations capture the first-order aspects of the splitting behaviour, full wavefield simulations will allow for more accurate modelling of SK(K)S splitting data, particularly in the presence of lateral heterogeneity. Lastly, we present a cross-verification test of AxiSEM3D against the SPECFEM3D_GLOBE spectral element solver for global seismic waves in an anisotropic earth model that includes both radial and azimuthal anisotropy. A nearly perfect agreement is achieved, with a significantly lower computational cost for AxiSEM3D. Our results highlight the capability of AxiSEM3D to handle arbitrary anisotropy geometries and its potential for future studies aimed at unraveling the details of anisotropy at the base of the mantle.

Simulation of seismic wave scattering for the computation of probabilistic coda-wave sensitivity kernels

<p>Scattered seismic coda waves are frequently used to characterize small scale medium heterogeneities, intrinsic attenuation or temporal changes of wave velocity. Spatial variability of these properties raises questions about the spatial sensitivity of seismic coda waves. Especially the continuous monitoring of medium perturbations using ambient seismic noise led to a demand for approaches to image perturbations observed with coda waves. An efficient approach to localize the property variations in the medium is to invert the observations from different source-receiver combinations and different lapse times in the coda for the location of the perturbations. The key of such an inversion is calculating the coda-wave sensitivity kernels which describe the connection between observations and the perturbation. Most discussions of sensitivity kernels use the acoustic approximation and assume wave propagation in the diffusion regime.</p><p>We model 2-D  elastic multiple nonisotropic scattering in a random medium with spatially variable heterogeneity and attenuation. The Monte Carlo method is used to numerically solve the radiative transfer equation that describes the wave scattering process here. Recording of the specific intensity of the wavefield <strong><em>I</em>(<em>r,n,t</em>)</strong> which contains the complete information about the energy at position <strong><em>r</em></strong> at time <em>t</em> with the propagation direction <strong><em>n</em></strong> allows us to calculate sensitivity kernels according to rigorous theoretical derivations. We investigate sensitivity kernels that describe the relationships between changes of the model parameters P- and S-wave velocity, P- and S-wave attenuation, and the strength of fluctuation on the one hand and the observables envelope amplitude, travel time changes and decorrelation on the other hand. These sensitivity kernels reflect the effect of the spatial variations of medium properties on wavefield. Our work offers a direct approach to compute these new expressions and adapt them to spatially variable heterogeneities. The sensitivity kernels we derived are the first step in the development of an inversion approach based on coda waves.</p>