Modeling an immune response to influenza A virus infection in alveolar epithelial cells

Influenza A viruses (IAV) have been the cause of several influenza pandemics in history and are a significant threat for the next global pandemic. Hospitalized influenza patients often have excess interferon production and a dysregulated immune response to the IAV infection. Obtaining a better understanding of the mechanisms of IAV infection that induce these harmful effects would help drug developers and health professionals create more effective treatments for IAV infection and improve patient outcomes. IAV stimulates viral sensors and receptors expressed by alveolar epithelial cells, like RIG-I and toll-like receptor 3 (TLR3). These two pathways coordinate with one another to induce expression of type III interferons to combat the infection. Presented here is a queuing theory-based model of these pathways that was designed to analyze the timing and amount of interferons produced in response to IAV single stranded RNA and double-stranded RNA detection. The model accurately represents biological data showing the necessary coordination of the RIG-I and TLR3 pathways for effective interferon production. This model can serve as the framework for future studies of IAV infection and identify new targets for potential treatments.     

Using ecological networks to answer questions in global biogeography and ecology

Ecological networks have classically been studied at site and landscape scales, yet recent efforts have been made to collate these data into global repositories. This offers an opportunity to integrate and upscale knowledge about ecological interactions from local to global scales to gain enhanced insights from the mechanistic information provided by these data. By drawing on existing research investigating patterns in ecological interactions at continental to global scales, we show how data on ecological networks, collected at appropriate scales, can be used to generate an improved understanding of many aspects of ecology and biogeography—for example, species distribution modelling, restoration ecology and conservation. We argue that by understanding the patterns in the structure and function of ecological networks across scales, it is possible to enhance our understanding of the natural world.     

具有节点偏置的高阶神经网络模型

在汪涛文献基础上提出了一个具有节点偏置的高阶神经网络模型、给出了模型的哈密顿量和学习算法,证明了学习算法的收敛性,该模型能对每一神经元自动引入一个节点偏置使得网络能够存储所有学习图样包括相关图样,其存储容量远高于Hebb—rule—like学习算法下的高阶神经网络模型.对由30个神经元组成的二阶神经网络进行了计算机仿真,结果证实了上述结论.此外,对初始突触强度对学习效果的影响和不同存储图样数目下的平均吸引半径进行了仿真计算并分析了所得结果.新模型的特点使其具有良好的应用前景     

采用Reichardt运动检测器和Boltzmann Machine神经网络的运动计算

建立了一个探讨灵长类视皮层从Ｖ１区到ＭＴ区的运动信息加工原理的计算模型,这个过程的突出特征是视觉运动信息经过了从局部检测进步到整体感知。模型的第一层由用于抽提运动模式的局部速度以及结构性质的Ｒｅｉｃｈａｒｄｔ运动检测器组成,进一步的加工是通过Ｂｏｌｔｚｍａｎｎ Ｍａｃｈｉｎｅ神经网络来实现的。这种网络的学习算法具有局部更新的显著性质,在学习阶段,网络不断地修改联结权重以形成对于记录在网络的显单元上     

N-Heterocyclic Carbene-Palladium Polymers Network: Recyclable Pre-catalyst for Effective Suzuki–Miyaura Coupling Reaction in Water

Rational design of high-efficiency N-heterocyclic carbene (NHC) palladium catalyst is of great importance to modern organic synthesis, especially in chemical and pharmaceutical industries. Herein, we fabricate a polymer network containing N-heterocyclic carbene palladium (PNNHC-Pd) catalytic active sites via an immobilization process. The N-heterocyclic carbene palladium can serve as a promising linkage of polymer network as well as an effective catalytic active site owing to its structural preference and strong σ-donating ability with palladium species. The results display that N-heterocyclic carbene palladium disperses homogeneously in polymer network, thus rendering PNNHC-Pd excellent catalytic activity, high stability and superior reusability in palladium-catalyzed Suzuki–Miyaura coupling reaction in aqueous medium. This work provides a new insight into the development of heterogenization of homogeneous catalysts based on polymer network.     

Game theory-based stakeholder analysis of marine nature reserves and its case studies in Guangdong Province,China

Marine nature reserves (MNRs) play an important role in biodiversity conservation and ecosystem restoration, which are valuable for marine resource protection in China. Many stakeholders are involved in MNRs. The interactions between MNR managers and other stakeholders affect the effectiveness of MNR management. This work divided MNR stakeholders into three categories and applied the analytical hierarchy process (AHP) to identify the key stakeholders. The game model was set up to analyze the strategies of MNR staff and stakeholders. The situation of key stakeholders and management was studied in two MNRs in Guangdong Province through field research and questionnaires. The results showed that fishermen were the main stakeholders in the two cases. The strategies adopted by MNR staff to improve the efficiency of law enforcement and decrease the probability of illegal activities of fishermen are to increase penalties, improve the arrest rate of illegal activities, and reduce the cost of law enforcement per patrol. We suggested that the fishermen’s negative impacts could be minimized by establishing a market management system to reduce illegal earnings, a record-keeping system to share additional benefits only with the fishermen who comply with the law, and an association mechanism in the coastal province to control illegal fishing across provincial boundaries. Identifying key stakeholders and analyzing their relationships could be helpful to find out the best strategy for different MNRs. Building a community of shared interests among various stakeholders could be a promising way to resolve conflicts and improve management effectiveness in MNRs.     

Factors underpinning Iranian farmers’ intention to conserve biodiversity at the farm level

The depletion of biodiversity is a major environmental challenge of the present time, with the agricultural sector being a significant contributor to the loss. Therefore, farmers' decisions and practices are pivotal in safeguarding biodiversity at the farm level. Accordingly, this study aimed to unravel the key factors influencing farmers' intention to conserve biodiversity (ICB), and in doing so, it adopted the protection motivation theory (PMT) as the theoretical framework. The study's statistical population comprised 412 farmers residing in Bavi County in the southwestern region of Iran. The research employed a questionnaire as the data collection instrument, and the research hypotheses were tested using structural equation modeling (SEM). The findings indicated that the respondents held a perception of high costs associated with implementing biodiversity conservation behaviors. They believed that such measures would not be easy to implement, and they would incur both monetary and non-monetary costs. Furthermore, the findings of the study revealed that the variables of SE and ICB among the studied farmers were lower than the theoretical median of 3, indicating that farmers may not have a strong desire and self-efficacy to engage in biodiversity conservation practices. Through the application of SEM, the study determined that the PMT accounts for 65.2% of farmers’ ICB. Additionally, perceived severity and self-efficacy were found to have the strongest effects on farmers’ ICB (as the first and second determinants), highlighting the importance of these variables in predicting farmers’ conservation behaviors. Overall, it can be concluded that these results have significant implications for policymakers in the agricultural sector, providing valuable insights into how to promote biodiversity conservation practices among farmers.