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Folic acid-modified carboxymethyl chitosan was polymerized withN-3-acrylamidophenylboronic acid monomer to prepare tumor-targeting nanoparticles (NPs). Nanoparticles’ size was characterized by dynamic light scattering, while the micromorphology was observed through transmission electron microscopy (TEM) and scanning electronic microscope (SEM). Doxorubicin was then loaded into prepared nanoparticles. The cellular uptake of these NPs was investigated in monolayer cell model and multicellular spheroids. The results revealed that FA-modified nanoparticles possess excellent ability of accumulation and penetration in 2D and 3D cell models. It was also found that these nanoparticles enhanced drug accumulation in tumor, which finally increased antitumor efficiency in H22 tumor-bearing mice. [ABSTRACT FROM PUBLISHER]

Data management and analysis are challenging with big Earth observation (EO) data. Expanding upon the rising promises of data cubes for analysis-ready big EO data, we propose a new geospatial infrastructure layered over a data cube to facilitate big EO data management and analysis. Compared to previous work on data cubes, the proposed infrastructure, GeoCube, extends the capacity of data cubes to multi-source big vector and raster data. GeoCube is developed in terms of three major efforts: formalize cube dimensions for multi-source geospatial data, process geospatial data query along these dimensions, and organize cube data for high-performance geoprocessing. This strategy improves EO data cube management and keeps connections with the business intelligence cube, which provides supplementary information for EO data cube processing. The paper highlights the major efforts and key research contributions to online analytical processing for dimension formalization, distributed cube objects for tiles, and artificial intelligence enabled prediction of computational intensity for data cube processing. Case studies with data from Landsat, Gaofen, and OpenStreetMap demonstrate the capabilities and applicability of the proposed infrastructure. [ABSTRACT FROM AUTHOR]

Artificial Intelligence Machine Learning (AI/ML), in particular Deep Learning (DL), is reorienting and transforming Earth Observation (EO). A consistent data model for delivery of training data will support the FAIR data principles (findable, accessible, interoperable, reusable) and enable Web-based use of training data in a spatial data infrastructure (SDI). Existing training datasets, including open source benchmark datasets, are usually packaged into public or personal repositories and lack discoverability and accessibility. Moreover, there is no unified method to describe the training data. Here we propose a training data model for AI in EO to allow documentation, storage, and sharing of geospatial training data in a distributed infrastructure. We present design rationales, information models, and an encoding method. Several scenarios illustrate the intended uses and benefits for EO DL applications in an open Web environment. The relationship with Open Geospatial Consortium (OGC) standards is also discussed, as is the impact on an AI-ready SDI. [ABSTRACT FROM AUTHOR]

In this paper, we address the distributed resilient fusion filtering (DRFF) problem for a class of nonlinear multi-sensor networked systems (MSNSs) with random sensor delay (RSD) under round-robin protocol (RRP). The RSD is depicted by the Bernoulli random variable with known occurrence probability. In order to relieve the network congestion, the RRP that can deal with information overload issue of the transmission process from sensor to the estimator is utilised. The major objective of this paper is that the resilient fusion filter is designed for nonlinear MSNSs with RSD and RRP in the light of the inverse covariance intersection approach, where the local upper bound regarding the filtering error covariance is obtained and then minimised by suitably exploiting the local filter gain. Finally, a simulation example that can show the validity of the provided DRFF algorithm is presented. [ABSTRACT FROM AUTHOR]