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Objective.Retinal prosthetics offer partial restoration of sight to patients blinded by retinal degenerative diseases through electrical stimulation of the remaining neurons. Decreasing the pixel size enables increasing prosthetic visual acuity, as demonstrated in animal models of retinal degeneration. However, scaling down the size of planar pixels is limited by the reduced penetration depth of the electric field in tissue. We investigated 3-dimensional (3d) structures on top of photovoltaic arrays for enhanced penetration of the electric field, permitting higher resolution implants.Approach.3D COMSOL models of subretinal photovoltaic arrays were developed to accurately quantify the electrodynamics during stimulation and verified through comparison to flat photovoltaic arrays. Models were applied to optimize the design of 3D electrode structures (pillars and honeycombs). Return electrodes on honeycomb walls vertically align the electric field with bipolar cells for optimal stimulation. Pillars elevate the active electrode, thus improving proximity to target neurons. The optimized 3D structures were electroplated onto existing flat subretinal prostheses.Main results.Simulations demonstrate that despite exposed conductive sidewalls, charge mostly flows via high-capacitance sputtered iridium oxide films topping the 3D structures. The 24μm height of honeycomb structures was optimized for integration with the inner nuclear layer cells in the rat retina, whilst 35μm tall pillars were optimized for penetrating the debris layer in human patients. Implantation of released 3D arrays demonstrates mechanical robustness, with histology demonstrating successful integration of 3D structures with the rat retinain-vivo.Significance. Electroplated 3D honeycomb structures produce vertically oriented electric fields, providing low stimulation thresholds, high spatial resolution, and high contrast for pixel sizes down to 20μm. Pillar electrodes offer an alternative for extending past the debris layer. Electroplating of 3D structures is compatible with the fabrication process of flat photovoltaic arrays, enabling much more efficient retinal stimulation.

 

Mobile wireless networks present several challenges for any learning system, due to uncertain and variable device movement, a decentralized network architecture, and constraints on network resources. In this work, we use deep reinforcement learning (DRL) to learn a scalable and generalizable forwarding strategy for such networks. We make the following contributions: i) we use hierarchical RL to design DRL packet agents rather than device agents, to capture the packet forwarding decisions that are made over time and improve training efficiency; ii) we use relational features to ensure generalizability of the learned forwarding strategy to a wide range of network dynamics and enable offline training; and iii) we incorporate both forwarding goals and network resource considerations into packet decision-making by designing a weighted DRL reward function. Our results show that our DRL agent often achieves a similar delay per packet delivered as the optimal forwarding strategy and outperforms all other strategies including state-of-the-art strategies, even on scenarios on which the DRL agent was not trained. 

Kaposi's sarcoma–associated herpesvirus (KSHV) inhibitor of cyclic GMP–AMP synthase (cGAS) (KicGAS) encoded by ORF52 is a conserved major tegument protein of KSHV and the first reported viral inhibitor of cGAS. In our previous study, we found that KicGAS is highly oligomerized in solution and that oligomerization is required for its cooperative DNA binding and for inhibiting DNA-induced phase separation and activation of cGAS. However, how KicGAS oligomerizes remained unclear. Here, we present the crystal structure of KicGAS at 2.5 Å resolution, which reveals an “L”-shaped molecule with each arm of the L essentially formed by a single α helix (α1 and α2). Antiparallel dimerization of α2 helices from two KicGAS molecules leads to a unique “Z”-shaped dimer. Surprisingly, α1 is also a dimerization domain. It forms a parallel dimeric leucine zipper with the α1 from a neighboring dimer, leading to the formation of an infinite chain of KicGAS dimers. Residues involved in leucine zipper dimer formation are among the most conserved residues across ORF52 homologs of gammaherpesviruses. The self-oligomerization increases the valence and cooperativity of interaction with DNA. The resultant multivalent interaction is critical for the formation of liquid condensates with DNA and consequent sequestration of DNA from being sensed by cGAS, explaining its role in restricting cGAS activation. The structure presented here not only provides a mechanistic understanding of the function of KicGAS but also informs a molecular target for rational design of antivirals against KSHV and related viruses. 

Critical role of hydrogen bond network is unraveled by integrating nuclear quantum effects into nonadiabatic molecular dynamics. 

Crowd mobility prediction, in particular, forecasting flows at and transitions across different locations, is essential for crowd analytics and management in spacious environments featured with large gathering. We propose GAEFT, a novel crowd mobility analytics system based on the multi-task graph attention neural network to forecast crowd flows (inflows/outflows) and transitions. Specifically, we leverage the collective and sanitized campus Wi-Fi association data provided by our university information technology service and conduct a relatable case study. Our comprehensive data analysis reveals the important challenges of sparsity and skewness, as well as the complex spatio-temporal variations within the crowd mobility data. Therefore, we design a novel spatio-temporal clustering method to group Wi-Fi access points (APs) with similar transition features, and obtain more regular mobility features for model inputs. We then propose an attention-based graph embedding design to capture the correlations among the crowd flows and transitions, and jointly predict the AP-level flows as well as transitions across buildings and clusters through a multi-task formulation. Extensive experimental studies using more than 28 million association records collected during 2020-2021 academic year validate the excellent accuracy of GAEFT in forecasting dynamic and complex crowd mobility. 

Amorphous inorganic semiconductors have attracted growing interest due to their unique electrical and optical properties that arise from their intrinsic disordered structure and thermodynamic metastability. Recently, amorphous inorganic semiconductors have been applied in a variety of new technologies, including solar cells, photoelectrocatalysis, and photocatalysis. It has been reported that amorphous phases can improve both efficiency and stability in these applications. While these phenomena are well established, their mechanisms have long remained unclear. This review first introduces the general background of amorphous inorganic semiconductor properties and synthesis. Then, the recent successes and current challenges of amorphous inorganic semiconductor-based materials for applications in solar cells, photoelectrocatalysis, and photocatalysis are addressed. In particular, we discuss the mechanisms behind the remarkable performances of amorphous inorganic semiconductors in these fields. Finally, we provide insightful perspectives into further developments for applications of amorphous inorganic semiconductors.