In vivo Kinetic Biodistribution of Nano‐Sized Outer Membrane Vesicles Derived from Bacteria

Evaluation of kinetic distribution and behaviors of nanoparticles in vivo provides crucial clues into their roles in living organisms. Extracellular vesicles are evolutionary conserved nanoparticles, known to play important biological functions in intercellular, inter‐species, and inter‐kingdom communication. In this study, the first kinetic analysis of the biodistribution of outer membrane vesicles (OMVs)—bacterial extracellular vesicles—with immune‐modulatory functions is performed. OMVs, injected intraperitoneally, spread to the whole mouse body and accumulate in the liver, lung, spleen, and kidney within 3 h of administration. As an early systemic inflammation response, increased levels of TNF‐α and IL‐6 are observed in serum and bronchoalveolar lavage fluid. In addition, the number of leukocytes and platelets in the blood is decreased. OMVs and cytokine concentrations, as well as body temperature are gradually decreased 6 h after OMV injection, in concomitance with the formation of eye exudates, and of an increase in ICAM‐1 levels in the lung. Following OMV elimination, most of the inflammatory signs are reverted, 12 h post‐injection. However, leukocytes in bronchoalveolar lavage fluid are increased as a late reaction. Taken together, these results suggest that OMVs are effective mediators of long distance communication in vivo.     

Adaptive actuator failure compensation design for unknown chaotic multi‐input systems

In this paper, an adaptive control approach is designed for compensating the faults in the actuators of chaotic systems and maintaining the acceptable system stability. We propose a state‐feedback model reference adaptive control scheme for unknown chaotic multi‐input systems. Only the dimensions of the chaotic systems are required to be known. Based on Lyapunov stability theory, new adaptive control laws are synthesized to accommodate actuator failures and system nonlinearities. An illustrative example is studied. The simulation results show the effectiveness of the design method. Copyright © 2014 John Wiley & Sons, Ltd.     

Barium titanate‐based nanodielectrics of two chemically recyclable thermosets

In this work was investigated the effect of the addition of barium titanate (BaTiO₃) on electrical properties of two chemically recyclable thermosets, polyhemiaminal (PHA) and polyhexahydro‐s‐triazine (PHT), both fabricated from 4,4′‐oxydianiline (ODA), an ether derivative of aniline and paraformaldehyde. Thermal and mechanical properties as well as chemical recyclability of the two polymers and their nanocomposites/nanodielectrics were also investigated. In addition, a quantitative analysis was conducted of the nanoparticle dispersion in the PHA‐/PHT‐based BaTiO₃‐containing nanocomposites using transmission electron microscopy imaging and the nearest‐neighbor distance index and this index was used to analyze the investigated properties in connection with the proper mechanisms. Regarding the electrical properties for both neat polymers, conductivity values of the order of 10^?8 S m^?1 at 100 Hz were observed and dielectric constant values close to 2.80 for both polymers at 1 kHz. The addition of 0.5 wt% of BaTiO₃ ferroelectric nanoparticles increased by about 44% the dielectric constant (1 kHz) and conductivity (10² Hz) of the PHA‐based nanocomposite. PHA and PHT exhibited glass transition temperature (T_g) values in the range 125–180 °C. An increase of 7 °C in T_g was observed after the incorporation of 0.5 wt% of BaTiO₃ into PHA. Concerning the mechanical properties, values in the range 4.00–4.45 GPa for reduced modulus and 0.30–0.43 GPa for nanohardness for PHA and PHT polymers were observed. Independently of filler content or polymer matrix, both mechanical properties were enhanced after the addition of BaTiO₃. The chemical recycling of PHA/PHT and all nanocomposites in the initial ODA reagent after sulfuric acid treatment was successfully characterized using the NMR and Fourier transform infrared spectroscopic techniques. © 2018 Society of Chemical Industry     

Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric

Machine Learning - We consider the problem of learning a binary classifier from only positive and unlabeled observations (called PU learning). Recent studies in PU learning have shown superior...