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Direct aminolysis of selenoester in aqueous media was investigated as a glycopeptide ligation strategy. This strategy allows the peptide and glycopeptide ligation to proceed smoothly (even with hindered amino acids) without the need of cysteine residue, N-terminal thiol auxiliary or coupling additive, and to afford the corresponding amide products in excellent yields. No epimerization was observed during ligation reations. In this work, the selenoester of unprotected glycopeptide was readily prepared, and the direct aminolysis of glycopeptide selenoester was successfully applied to synthesize MUC1 mucin sequence efficiently.

Urea electrolysis enables simultaneous energy-saving hydrogen production and wastewater purification at a small thermodynamic potential of 0.37 V; however, the total energy-utilization efficiency is still substantially restricted by the sluggish kinetics of the urea oxidization reaction (UOR) at the anode. In this work, an electrochemical tuning strategy has been employed to obtain a Ni (III)-enriched catalyst by starting from NiMoO4∙xH2O nanorod arrays. It has been found that the electrochemical tuning not only modifies the valency state of Ni but also causes a dramatic change in stoichiometry and microstructure of the material, leading to significantly improved activities for UOR under alkaline conditions. Notably, the obtained catalyst delivers a current density of 100 mA cm−2 for UOR at the potential of only 1.36 V vs RHE, which surpasses the best-reported values for UOR catalysis at the same applied potential, demonstrating the great promise of the electrochemical tuning strategy in rational modifications of material towards promoted electrocatalysis.

Summary Many cancer vaccines are not successful in clinical trials, mainly due to the challenges associated with breaking immune tolerance. Herein, we report a new strategy using an adjuvant-protein-antigen (three-in-one protein conjugates with built-in adjuvant) as an anticancer vaccine, in which both the adjuvant (small-molecule TLR7 agonist) and tumor-associated antigen (mucin 1, MUC1) are covalently conjugated to the same carrier protein (BSA). It is shown that the protein conjugates with built-in adjuvant can increase adjuvant's stimulation, prevent adjuvant's systemic toxicities, facilitate the codelivery of adjuvants and antigens, and enhance humoral and cellular immune responses. The IgG antibody titers elicited by the self-adjuvanting three-in-one protein conjugates were significantly higher than those elicited by the vaccine mixed with TLR7 agonist (more than 15-fold) or other traditional adjuvants. Importantly, the potent immune responses against cancer cells suggest that this new vaccine construct is an effective strategy for the personalized antitumor immunotherapy., Graphical Abstract, Highlights • Adjuvant-protein-antigen protein conjugates act as new cancer vaccine strategy • Built-in adjuvant of TLR7 agonist can reduce toxicities and enhance immune stimulations • Three-in-one protein conjugates boost potent immune responses against cancer cells, Conjugate; Biochemistry; Immunology

Human enterovirus 71 (EV71) is a cause of hand, foot and mouth disease (HMFD) in children under 6 years old, and could cause serious neurological complications in some patients. Numerous large outbreaks of EV71 caused HMFD have occurred recently in Asia, especially in China. The cross-reactivity of EV71 with human brain tissue was observed and the cross-reactivity inducing regions were identified in previously study, which suggested that there were two regions in structural proteins of virus should be avoided in the vaccine. Six peptides without cross-reactivity were selected and combined into three vaccine candidates and applied in further evaluation in neonatal mice. The Vac6 comprising the peptides of P(70-159), P(140-249), P(324-443) and P(746-876) of the structural proteins could provide effective protection on pups against virus infection, as shown in viral copies detection and histopathology examination. Immunohistochemical staining results indicated that Vac6 had no cross-reactivity with human brain tissues. Our results suggested that Vac6 could have potential clinical value against EV71 epidemics caused mainly by C4 strains in the mainland of China.

An adaptive fuzzy Q-learning (AFQL) based on fuzzy inference systems (FIS) is proposed. The FIS realized by a normalized radial basis function (NRBF) neural network is used to approach Q-value function, whose input is composed of state and action. The rules of FIS are created incrementally according to the novelty of each element of the pair of state-action. Moreover the premise part and consequent part of the FIS are updated using extended Kalman filter (EKF). The action that impacts on environment is the one with maximum output of FIS in the current state and generated through optimization method. Simulation results in the wall-following task of mobile robots and the inverted pendulum balancing problem demonstrate that the superiority and applicability of the proposed AFQL method.

An indirect immunocolorimetric assay (ICA) to detect rubella virus infected cells by the naked eye was developed. This assay was as sensitive and specific as an indirect immunofluorescent assay (IFA), could detect as little as 10 plaque forming units (pfu) of rubella virus in the initial inoculum and could detect viruses from throat swabs. This assay detected infection with viruses of all nine rubella virus genotypes available for testing. It could be utilized for virus quantitation and a neutralization assay. In addition to detecting rubella virus infection, this assay also allowed us to confirm measles virus infection. This assay should be useful for virus isolation from clinical samples.

In recent years, the poly-L-proline type II (PPII) conformation has gained more and more importance. This structure plays vital roles in many biological processes. But few studies have been made to predict PPII secondary structures computationally. The support vector machine (SVM) represents a new approach to supervised pattern classification and has been successfully applied to a wide range of pattern recognition problems. In this paper, we present a SVM prediction method of PPII conformation based on local sequence. The overall accuracy for both the independent testing set and estimate of jackknife testing reached approximately 70%. Matthew's correlation coefficient (MCC) could reach 0.4. By comparing the results of training and testing datasets with different sequence identities, we suggest that the performance of this method correlates with the sequence identity of dataset. The parameter of SVM kernel function was an important factor to the performance of this method. The propensities of residues located at different positions were also analyzed. By computing Z-scores, we found that P and G were the two most important residues to PPII structure conformation.