Meningitic Escherichia coli K1 Penetration and Neutrophil Transmigration Across the Blood–Brain Barrier are Modulated by Alpha7 Nicotinic Receptor

Alpha7 nicotinic acetylcholine receptor (nAChR), an essential regulator of inflammation, is abundantly expressed in hippocampal neurons, which are vulnerable to bacterial meningitis. However, it is unknown whether α7 nAChR contributes to the regulation of these events. In this report, an aggravating role of α7 nAChR in host defense against meningitic E. coli infection was demonstrated by using α7-deficient (α7-/-) mouse brain microvascular endothelial cells (BMEC) and animal model systems. As shown in our in vitro and in vivo studies, E. coli K1 invasion and polymorphonuclear neutrophil (PMN) transmigration across the blood-brain barrier (BBB) were significantly reduced in α7-/- BMEC and α7-/- mice. Stimulation by nicotine was abolished in the α7-/- cells and animals. The same blocking effect was achieved by methyllycaconitine (α7 antagonist). The tight junction molecules occludin and ZO-1 were significantly reduced in the brain cortex of wildtype mice infected with E. coli and treated with nicotine, compared to α7-/- cells and animals. Decreased neuronal injury in the hippocampal dentate gyrus was observed in α7-/- mice with meningitis. Proinflammatory cytokines (IL-1β, IL-6, TNFα, MCP-1, MIP-1alpha, and RANTES) and adhesion molecules (CD44 and ICAM-1) were significantly reduced in the cerebrospinal fluids of the α7-/- mice with E. coli meningitis. Furthermore, α7 nAChR is the major calcium channel for nicotine- and E. coli K1-increased intracellular calcium concentrations of mouse BMEC. Taken together, our data suggest that α7 nAChR plays a detrimental role in the host defense against meningitic infection by modulation of pathogen invasion, PMN recruitment, calcium signaling and neuronal inflammation

Comparison of linear and circular polarization in foggy environments at UV-NIR wavelengths

This paper investigates the polarization persistence of linear polarization and circular polarization in foggy environments from ultraviolet (UV) to near-infrared (NIR). Using polarization tracking Monte Carlo simulation for varying particle size, wavelength, refractive index, and detection distance, it is shown that linear polarization and circular polarization exhibit different persistence performance. For wet haze of 0.6 μm mean diameter particles, right-handed circular polarization shows better persistence than parallel polarization at wavelengths of 0.36, 0.543 and 1.0 μm. But parallel polarization shows better persistence at wavelengths of 1.55, 2.1 and 2.4 μm. For wet haze of 1.0 μm mean diameter particles, right-handed circular polarization shows better persistence at wavelengths of 0.36, 0.543, 1.0 and 1.55 μm. But parallel polarization shows better persistence at wavelengths of 2.1 and 2.4 μm. For wet haze of 2.0 μm particles and radiation fog and advection fog, right-handed circular polarization shows better persistence at all simulated wavelengths. In short, right-handed circular polarization persists better than parallel polarization in most scenarios, however, with increasing wavelength and decreasing particle size, parallel polarization gradually persists better than right-handed circular polarization. Finally, anisotropy factor for various particle models is used to map the propagation law of right-handed circular polarization and parallel polarization

Building Function Recognition Using the Semi-Supervised Classification

The functional classification of buildings is important for creating and managing urban zones and assisting government departments. Building function recognition is incredibly valuable for wide applications ranging from the determination of energy demand. By aiming at the topic of urban function classification, a semi-supervised graph structure network combined unified message passing model was introduced. The data of this model include spatial location distribution of buildings, building characteristics and the information mined from points of interesting (POIs). In order to extract the context information, each building was regarded as a graph node. Building characteristics and corresponding POIs information were embedded to mine the building function by the graph convolutional neural network. When training the model, several node labels in the graph were masked, and then these labels were predicted by the trained model so that this work could take full advantage of the node label and the feature information of all nodes in both the training and prediction stages. Quasi-experiments proved that the proposed method for building function classification using multi-source data enables the model to capture more meaningful information with limited labels, and it achieves better function classification results

Building Function Recognition Using the Semi-Supervised Classification

The functional classification of buildings is important for creating and managing urban zones and assisting government departments. Building function recognition is incredibly valuable for wide applications ranging from the determination of energy demand. By aiming at the topic of urban function classification, a semi-supervised graph structure network combined unified message passing model was introduced. The data of this model include spatial location distribution of buildings, building characteristics and the information mined from points of interesting (POIs). In order to extract the context information, each building was regarded as a graph node. Building characteristics and corresponding POIs information were embedded to mine the building function by the graph convolutional neural network. When training the model, several node labels in the graph were masked, and then these labels were predicted by the trained model so that this work could take full advantage of the node label and the feature information of all nodes in both the training and prediction stages. Quasi-experiments proved that the proposed method for building function classification using multi-source data enables the model to capture more meaningful information with limited labels, and it achieves better function classification results

Preparation and Characterization of Microcrystalline Cellulose/Polylactic Acid Biocomposite Films and Its Application in Lanzhou Lily (<i>Lilium davidii var. unicolor</i>) Bulbs Preservation

Green biodegradable bio-based films have gained interest in replacing petroleum-derived plastic packaging materials as a new preservation strategy for fruits and vegetables to alleviate environmental pressures. In this study, we aimed to develop novel biodegradable composite films based on microcrystalline cellulose (MCC) reinforced polylactic acid (PLA). Our results demonstrated that the addition of 3% MCC to PLA could improve its tensile strength. Scanning electron microscopy analysis revealed that MCC dispersed well in PLA at lower content while agglomerated at higher content. It was discovered that all four types of MCC/PLA biocomposite films could retard the color change of Lanzhou lily bulbs, accompanied by maintaining favorable total soluble solid, total sugar, total polyphenols, and flavonoid content, inhibiting the activities of phenylalanine ammonia-lyase and the content of malondialdehyde during storage. Moreover, the preservation effect of MCC/PLA biocomposite films on Lanzhou lily bulbs was evaluated using a membership function, and the SSS MCC/PLA biocomposite film demonstrated a favorable fresh-keeping effect. In conclusion, four types of MCC from different biomass materials added to PLA-based products can be beneficial in improving the attractive properties of biocomposite films. These films are expected to replace petroleum-derived plastics as a new packaging material for preserving Lanzhou lily bulbs