Indocyanine Green-Loaded Polydopamine-Reduced Graphene Oxide Nanocomposites with Amplifying Photoacoustic and Photothermal Effects for Cancer Theranostics

Photoacoustic (PA) imaging and photothermal therapy (PTT) as light-induced theranostic platforms have been attracted much attention in recent years. However, the development of highly efficient and integrated phototheranostic nanoagents for amplifying PA imaging and PTT treatments poses great challenges. Here, we report a novel phototheranostic nanoagent using indocyanine green-loaded polydopamine-reduced graphene oxide nanocomposites (ICG-PDA-rGO) with amplifying PA and PTT effects for cancer theranostics. The results demonstrate that the PDA layer coating on the surface of rGO could effectively absorb a large number of ICG molecules, quench ICG's fluorescence, and enhance the PDA-rGO's optical absorption at 780 nm. The obtained ICG-PDA-rGO exhibits stronger PTT effect and higher PA contrast than that of pure GO and PDA-rGO. After PA imaging-guided PTT treatments, the tumors in 4T1 breast subcutaneous and orthotopic mice models are suppressed completely and no treatment-induced toxicity being observed. It illustrates that the ICG-PDA-rGO nanocomposites constitute a new class of theranostic nanomedicine for amplifying PA imaging and PTT treatments

Augmented Genetic Algorithm V2 with Reinforcement Learning for PDN Decap Optimization

Genetic Algorithms (GAs) Use Many Hyperparameters, and Tuning These Parameters Can Determine the Optimization Performance. a GA with an Augmented Initial Population Was Proposed for Decap Optimization but It Had Convergence Issues by Getting Stuck in the Local Minimum. This Work Uses a Reinforcement Learning (RL) Approach to Adaptively Tune the Hyperparameters of GA during its Operation. with This Approach, the Agent Tries to Change the Parameters So that the GA Does Not Get Stuck in the Local Minimum. the Proposed Method Combining the RL Agent and Augmented GA Showed Better Performance in Terms of Solution Quality and Time Cost. overall, in All the Cases Tested, the Proposed Method Showed Better Performance Than the Augmented GA Without RL

Riemannian Surface on Carbon Anodes Enables Li-Ion Storage at −35 °C

Since sluggish Li$^{+}$ desolvation leads to severe capacity degradation of carbon anodes at subzero temperatures, it is urgently desired to modulate electron configurations of surface carbon atoms toward high capacity for Li-ion batteries. Herein, a carbon-based anode material (O-DF) was strategically synthesized to construct the Riemannian surface with a positive curvature, which exhibits a high reversible capacity of 624 mAh g$^{-1}$ with an 85.9% capacity retention at 0.1 A g$^{-1}$ as the temperature drops to −20 °C. Even if the temperature drops to −35 °C, the reversible capacity is still effectively retained at 160 mAh g$^{-1}$ after 200 cycles. Various characterizations and theoretical calculations reveal that the Riemannian surface effectively tunes the low-temperature sluggish Li$^{+}$ desolvation of the interfacial chemistry via locally accumulated charges of non-coplanar sp$^{x}$ (2 < x < 3) hybridized orbitals to reduce the rate-determining step of the energy barrier for the charge-transfer process. Ex-situ measurements further confirm that the sp$^{x}$-hybridized orbitals of the pentagonal defect sites should denote more negative charges to solvated Li$^{+}$ adsorbed on the Riemannian surface to form stronger Li–C coordinate bonds for Li$^{+}$ desolvation, which not only enhances Li-adsorption on the curved surface but also results in more Li$^{+}$ insertion in an extremely cold environment

PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and the reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques. Our design goal for PyPose is to make it user-friendly, efficient, and interpretable with a tidy and well-organized architecture. Using an imperative style interface, it can be easily integrated into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves 3-20$\times$ speedup in computation compared to state-of-the-art libraries. To boost future research, we provide concrete examples across several fields of robotics, including SLAM, inertial navigation, planning, and control

Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease

Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large‐scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting‐state blood‐oxygen‐level‐dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical–cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia

Far-Field Radiation Estimation from Near-Field Measurements and Image Theory

This paper proposed a near-field to far-field transformation method for the radiation sources located on a large ground plane, based on the Huygens\u27s principle and image theory. This method uses the tangential electromagnetic fields on a small near-field plane and the vertical electric fields, one tangential component (parallel to the ground plane) of magnetic fields around the near-field plane to extract the equivalent current sources. The far-field radiations are calculated from these equivalent sources and their images. The application of this method in several simulation models indicates that it has very good performance on both simple printed circuit boards and antenna radiation estimation. The method proposed in this paper can decrease the total scanning area in real measurements.