The development of contrast agents that can be activated by multiple modes is of great significance for tumor diagnosis. In this study, the lactoferrin (Lf)-conjugated polylactic acid (PLLA) nanobubbles (Lf-PLLA NBs) were used to encapsulate liquid perfluoropentane (PFP) with the double emulsion method, creating PFP loaded (PFP/ Lf-PLLA) NBs for the ultrasound/magnetic resonance dual-modality imaging of subcutaneous tumor. The particle diameter and stability of nanobubbles were investigated by photon correlation spectroscopy. The biocompatibility of nanobubbles was preliminarily evaluated by cell proliferation and migration assay, hemolysis rate, and blood biochemistry analysis. A B-mode clinical ultrasound real-time imaging system was used to perform ultrasonic imaging in vivo. Magnetic resonance imaging in vivo was applied with a clinical 3.0 T magnetic resonance imaging (MRI) scanner system. The mean particle diameter of PFP/Lf-PLLA NBs was 320.2 ± 4.1 nm with a low polydispersity index (PDI, 0.145 ± 0.025), and the NBs were negatively charged (−11.4 ± 0.4 mV). The transmission electron microscopy (TEM) results showed that PFP/Lf-PLLA NBs exhibited highly monodispersed and possessed an obvious spherical structure of nanocapsules. Nanobubbles had good stability at 4°C. Different concentrations of the PFP/Lf-PLLA NBs solution had no effect on the cell in cytotoxicity and cell migration, and the results of hemolysis rate and blood biochemistry assay also indicated the good biocompatibility of NBs. On the ultrasound/magnetic resonance imaging of tumor-bearing mice, PFP/Lf-PLLA NBs showed significantly enhanced contrast ability of tumor tissue. Therefore, PFP/Lf-PLLA NBs had great potential to be a contrast agent for tumor dual-modality imaging in vivo.
Most recurrent neural networks (RNNs) do not include a fundamental constraint of real neural circuits: Dale's Law, which implies that neurons must be excitatory (E) or inhibitory (I). Dale's Law is generally absent from RNNs because simply partitioning a standard network's units into E and I populations impairs learning. However, here we extend a recent feedforward bio-inspired EI network architecture, named Dale's ANNs, to recurrent networks, and demonstrate that good performance is possible while respecting Dale's Law. This begs the question: What makes some forms of EI network learn poorly and others learn well? And, why does the simple approach of incorporating Dale's Law impair learning? Historically the answer was thought to be the sign constraints on EI network parameters, and this was a motivation behind Dale's ANNs. However, here we show the spectral properties of the recurrent weight matrix at initialisation are more impactful on network performance than sign constraints. We find that simple EI partitioning results in a singular value distribution that is multimodal and dispersed, whereas standard RNNs have an unimodal, more clustered singular value distribution, as do recurrent Dale's ANNs. We also show that the spectral properties and performance of partitioned EI networks are worse for small networks with fewer I units, and we present normalised SVD entropy as a measure of spectrum pathology that correlates with performance. Overall, this work sheds light on a long standing mystery in neuroscience-inspired AI and computational neuroscience, paving the way for greater alignment between neural networks and biology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.