Opinion Evolution with Information Quality of Public Person and Mass Acceptance Threshold.

Public persons are nodes with high attention to public events, and their opinions can directly affect the development on events. However, because of rationality, the followers' acceptance to the public persons' opinions will depend on the information trait on public persons' opinions and own comprehension. To study how different opinions of the public persons guide different followers, we build an opinion dynamics model, which would provide a theoretical method for public opinion management. Based on the classical bounded confidence model, we extract the information quality variables and individual trust threshold and introduce them to construct our two-stage opinion evolution model. And then in the simulation experiments, we analyze the different effects of opinion information quality, opinion release time, and frequency on public opinion by adjusting the different parameters. Finally, we added a case to compare real data, the data from classical model simulation and the data from improved model simulation to verify the effectiveness on our model. The research found that the more sufficient the argument and the more moderate the attitude, the more likely to guide the public opinion. If public person holds different opinions and different information quality, he should choose different time to present his opinion to achieve ideal guide effect. When public person holds neutral opinion and the information quality is relatively general, he/she can intervene in public opinion as soon as possible to control final public opinion; when public person holds extreme opinion and the information quality is relatively high, he/she can choose to express opinion after a certain period on public opinion evolution, which is conducive to improve the guidance effect on public opinion. The frequency of releasing opinions of public person consistently has a positive impact on the final public opinion.

Multifunctional Supramolecular Vesicles as Zn2+ -Triggered Microglial Modulator Alleviates Alzheimer's Disease.

Zn2+ -induced ß-amyloid protein (Aß) aggregation and microglia activation are the predominant contributors in Alzheimer's disease (AD). Regulating intracephalic excessive Zn2+ is a promising therapeutic strategy for AD treatment. However, only inhibition of Zn2+ is hardly to repair continuous damages caused by activated microglia. Herein, an intelligent resveratrol-loaded supramolecular vesicles (RES-loaded vesicles) with zinc ion chelation function and responsive release capability are constructed to alleviate Aß fibrillation, oxidative stress, and microglial dysfunction. The resveratrol encapsulation efficiency and drug loading efficiency are calculated to be 49.67% and 7.87%, respectively. In vitro studies demonstrate that the RES-loaded vesicles can modulate Zn2+ -dependent Aß aggregation. More importantly, the cargoes will be released in zinc environment and further reprograms microglia from proinflammatory M1 phenotype toward anti-inflammatory M2 phenotype, which prevents spontaneous neuroinflammation and alleviates cytotoxicity of cultured cells from 29% to 12%. With the stereotactic or intranasal administration, RES-loaded vesicles can overcome the blood brain barrier, alleviate neuronal apoptosis, neuroinflammation, and ultimately ameliorate cognitive impairment in two AD mouse models. This work provides a new sight for taking advantage of Zn2+ to treat CNS disorders.

Opinion Dynamics Model with Bounded Confidence and the Sleeper Effect.

The evolution of opinions is a complex mechanism. The evolution of individual opinions is not only influenced by own and others but also by psychological effects. Therefore, based on the classical bounded confidence model and the sleeper effect, a new opinion evolution model is proposed in this paper. In this new opinion evolution model, we increase the opinions on every step and take into account the "discount opinions." Since mental states for human are not easy to measure, we assumed three different initial networks and carried out simulation experiments. To verify the rationality on our model, we compared the effects with and without the sleeper effect, different thresholds, and discounting opinion ratio on opinion aggregation and convergence. Finally, we found the sleeper effect can differently affect the convergence of opinions in different opinion environments.

Plant Viral Coat Proteins as Biochemical Targets for Antiviral Compounds.

Coat proteins (CPs) of RNA plant viruses play a pivotal role in virus particle assembly, vector transmission, host identification, RNA replication, and intracellular and intercellular movement. Numerous compounds targeting CPs have been designed, synthesized, and screened for their antiviral activities. This review is intended to fill a knowledge gap where a comprehensive summary is needed for antiviral agent discovery based on plant viral CPs. In this review, major achievements are summarized with emphasis on plant viral CPs as biochemical targets and action mechanisms of antiviral agents. This review hopefully provides new insights and references for the further development of new safe and effective antiviral pesticides.

Evolution analysis of online topics based on 'word-topic' coupling network.

Analyzing topic evolution is an effective way to monitor the overview of topic spreading. Existing methods have focused either on the intensity evolution of topics along a timeline or the topic evolution path of technical literature. In this paper, we aim to study topic evolution from a micro perspective, which not only captures the topic timeline but also reveals the topic status and the directed evolutionary path among topics. Firstly, we construct a word network by co-occurrence relationship between feature words. Secondly, Latent Dirichlet allocation (LDA) model is used to automatically extract topics and capture the mapping relationship between words and topics, and then a 'word-topic' coupling network is built. Thirdly, based on the 'word-topic' coupling network, we describe the topic intensity evolution over time and measure topic status considering the contribution of feature words to a topic. The concept of topic drifting probability is proposed to identify the evolutionary path. Experimental results conducted on two real-world data sets of "COVID-19" demonstrate the effectiveness of our proposed method.

Binding studies between cytosinpeptidemycin and the superfamily 1 helicase protein of tobacco mosaic virus.

Tobacco mosaic virus (TMV) helicases play important roles in viral multiplication and interactions with host organisms. They can also be targeted by antiviral agents. Cytosinpeptidemycin has a good control effect against TMV. However, the mechanism of action is unclear. In this study, we expressed and purified TMV superfamily 1 helicase (TMV-Hel) and analyzed its three-dimensional structure. Furthermore, the binding interactions of TMV-Hel and cytosinpeptidemycin were studied. Microscale thermophoresis and isothermal titration calorimetry experiments showed that cytosinpeptidemycin bound to TMV-Hel with a dissociation constant of 0.24-0.44 µM. Docking studies provided further insights into the interaction of cytosinpeptidemycin with the His375 of TMV-Hel. Mutational and Microscale thermophoresis analyses showed that cytosinpeptidemycin bound to a TMV-Hel mutant (H375A) with a dissociation constant of 14.5 µM. Thus, His375 may be the important binding site for cytosinpeptidemycin. The data are important for designing and synthesizing new effective antiphytoviral agents.