912 research outputs found
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
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