We report the one-step assembly of vaccine particles by encapsulating ovalbumin (OVA) and cytosine-phosphate-guanine oligodeoxynucleotides (CpG) into poly­(ethylene glycol) (PEG)-mediated zeolitic imidazolate framework-8 nanoparticles (OVA-CpG@ZIF-8 NPs), where PEG improves the stability and dispersity of ZIF-8 NPs and the NPs protect the encapsulated OVA and CpG to circumvent the cold chain issue. Compared with free OVA and OVA-encapsulated ZIF-8 (OVA@ZIF-8) NPs, OVA-CpG@ZIF-8 NPs can enhance antigen uptake, cross-presentation, dendritic cell (DC) maturation, production of specific antibody and cytokines, and CD4+ T and CD8+ T cell activation. More importantly, the vaccine particles retain their bioactivity against enzymatic degradation, elevated temperatures, and long-term storage at ambient temperature. The study highlights the importance of PEG-mediated ZIF-8 NPs as a vaccine delivery system for the promising application of effective and cold chain-independent vaccination against diseases.

Summary The robust fusion steady‐state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, one‐step random delay, missing measurements, and uncertain noise variances, the phenomena of one‐step random delay and missing measurements occur in a random way, and are described by two Bernoulli distributed random variables with known conditional probabilities. Using a model transformation approach, which consists of augmented approach, derandomization approach, and fictitious noise approach, the original multisensor system under study is converted into a multimodel multisensor system with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the optimal fusion algorithm weighted by matrices, the robust distributed weighted state fusion steady‐state Kalman estimators are derived for the considered system. In addition, by using the proposed model transformation approach, the centralized fusion system is obtained, furthermore the robust centralized fusion steady‐state Kalman estimators are proposed. The robustness of the proposed estimators is proved by using a combination method consisting of augmented noise approach, decomposition approach of nonnegative definite matrix, matrix representation approach of quadratic form, and Lyapunov equation approach, such that for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady‐state Kalman estimators are proved. An example with application to autoregressive signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.

Mineralization of metal-organic frameworks (MOFs) has shown potential in the encapsulation of functional biomacromolecules and therapeutics. One of the challenges is to improve the stability of MOFs in aqueous solution....

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. After the input weights and the hidden layer biases are chosen randomly, ELM can be simply considered a linear system. However, the learning time of ELM is mainly spent on calculating the Moore-Penrose inverse matrices of the hidden layer output matrix. This paper focuses on effective computation of the Moore-Penrose inverse matrices for ELM, several methods are proposed. They are the reduced QR factorization with column Pivoting and Geninv ELM (QRGeninv-ELM), tensor product matrix ELM (TPM-ELM). And we compare QRGeninv-ELM, TPM-ELM with the relational algorithm of Moore-Penrose inverse matrices for ELM, the relational algorithms are: Cholesky factorization of singular matrix ELM (Geninv-ELM), QR factorization and Ginv ELM (QRGinv-ELM), the conjugate Gram-Schmidt process ELM (CGS-ELM). The experimental results and the statistical analysis of the experimental results both demonstrate that QRGeninv-ELM, TPM-ELM and Geninv-ELM are faster than other kinds of ELM and can reach comparable generalization performance.

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