Ultrasound-targeted microbubble destruction enhances AAV mediated gene transfection: human RPE cells in vitro and the rat retina in vivo

The present study was performed to investigate the efficacy and safety of Ultrasound-targeted microbubble destruction (UTMD) mediated rAAV2-EGFP to cultured human retinal pigment epithelium (RPE) cells _in vitro_ and the rat retina _in vivo_. _In vitro_ study, cultured human RPE cells were exposed to US under different conditions with or without microbubbles. Furthermore, the effect of UTMD to rAAV2-EGFP itself and the cells were evaluated. _In vivo_ study, gene transfer was examined by injecting rAAV2-EGFP into the subretinal space of the rats with or without microbubbles and then exposed to US. We investigated EGFP expression _in vivo_ via stereomicroscopy and performed quantitative analysis by Axiovision 3.1 software. HE staining and frozen sections were used to observe tissue damage and location of EGFP gene expression. _In vitro_ study, the transfection efficiency of rAAV2-EGFP increased 74.85% under the optimal UTMD conditions. Furthermore, there was almost no cytotoxicity to the cells and rAAV2-EGFP itself. _In vivo_ study, UTMD could be used safely to enhance and accelerate transgene expression of the retina. Fluorescence expression was mainly located in the layer of retina. UTMD is a promising method for gene delivery to the retina

Ab initio study of the thermodynamic properties of rare-earthmagnesium intermetallics MgRE (RE=Y, Dy, Pr, Tb)

We have performed an ab initio study of the thermodynamical properties of rare-earth-magnesium intermetallic compounds MgRE (RE=Y, Dy, Pr, Tb) with CsCl-type B2-type structures. The calculations have been carried out the density functional theory and density functional perturbation theory in combination with the quasiharmonic approximation. The phonon-dispersion curves and phonon total and partial density of states have been investigated. Our results show that the contribution of RE atoms is dominant in phonon frequency, and this character agrees with the previous discussion by using atomistic simulations. The temperature dependence of various quantities such as the thermal expansions, bulk modulus, and the heat capacity are obtained. The electronic contributions to the specific heat are discussed, and found to be important for the calculated MgRE intermetallics.Comment: 12 pages, 6 figure

Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics

Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex 3D patient-specific shapes in the real world. First, it is notoriously challenging to parameterize the input space of arbitrarily complex 3-D geometries. Second, the process often involves massive forward simulations, which are extremely computationally demanding or even infeasible. We propose a novel deep learning surrogate modeling solution to address these challenges and enable rapid hemodynamic predictions. Specifically, a statistical generative model for 3-D patient-specific shapes is developed based on a small set of baseline patient-specific geometries. An unsupervised shape correspondence solution is used to enable geometric morphing and scalable shape synthesis statistically. Moreover, a simulation routine is developed for automatic data generation by automatic meshing, boundary setting, simulation, and post-processing. An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces. Numerical studies on aortic flows are conducted to demonstrate the effectiveness and merit of the proposed techniques.Comment: 8 figures, 2 table

Finding and Editing Multi-Modal Neurons in Pre-Trained Transformer

Multi-modal large language models (LLM) have achieved powerful capabilities for visual semantic understanding in recent years. However, little is known about how LLMs comprehend visual information and interpret different modalities of features. In this paper, we propose a new method for identifying multi-modal neurons in transformer-based multi-modal LLMs. Through a series of experiments, We highlight three critical properties of multi-modal neurons by four well-designed quantitative evaluation metrics. Furthermore, we introduce a knowledge editing method based on the identified multi-modal neurons, for modifying a specific token to another designative token. We hope our findings can inspire further explanatory researches on understanding mechanisms of multi-modal LLMs

Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving

Multi-view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are unavailable in many real-world scenarios, such as autonomous driving. In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings. Surprisingly, we find current multi-view depth estimation methods or single-view and multi-view fusion methods will fail when given noisy pose settings. To address this challenge, we propose a single-view and multi-view fused depth estimation system, which adaptively integrates high-confident multi-view and single-view results for both robust and accurate depth estimations. The adaptive fusion module performs fusion by dynamically selecting high-confidence regions between two branches based on a wrapping confidence map. Thus, the system tends to choose the more reliable branch when facing textureless scenes, inaccurate calibration, dynamic objects, and other degradation or challenging conditions. Our method outperforms state-of-the-art multi-view and fusion methods under robustness testing. Furthermore, we achieve state-of-the-art performance on challenging benchmarks (KITTI and DDAD) when given accurate pose estimations. Project website: https://github.com/Junda24/AFNet/.Comment: Accepted to CVPR 202