A resorcinarene for inhibition of Aβ fibrillation.

Amyloid-β peptides (Aβ) fibrillation is the hallmark of Alzheimer's disease (AD). However, it has been challenging to discover potent agents in order to inhibit Aβ fibrillation. Herein, we demonstrated the effect of resorcinarene on inhibiting Aβ fibrillation in vitro via experimental and computational methods. Aβ were incubated with different concentrations of resorcinarene so as to monitor the kinetics by using thioflavin T binding assay. The results, which were further confirmed by far-UV CD spectroscopy and atomic force microscopy, strongly indicated that the higher concentration of resorcinarene, the more effective the inhibition of Aβ fibrillation. A cytotoxicity study showed that when sea urchin embryos were exposed to the resorcinarene, the majority survived due to the resorcinarene low toxicity. In addition, when the resorcinarene was added, the formation of toxic Aβ 42 species was delayed. Computational studies of Aβ fibrillation, including docking simulations and MD simulations, illustrated that the interaction between inhibitor resorcinarene and Aβ is driven by the non-polar interactions. These studies display a novel strategy for the exploration of promising antiamyloiddogenic agents for AD treatments

Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from such data. Accordingly, graph neural networks (GNNs) indeed achieve impressive success in various fields. Revisiting the GNN learning paradigms, we discover that the relationship between human expertise and the knowledge modeled by GNNs still confuses researchers. To this end, we introduce motivating experiments and derive an empirical observation that the human expertise is gradually learned by the GNNs in general domains. By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance. By exploring the intrinsic mechanism behind such observations, we elaborate the Structural Causal Model for the graph representation learning paradigm. Following the theoretical guidance, we innovatively introduce the auxiliary causal logic learning paradigm to improve the model to learn the expertise logic causally related to the graph representation learning task. In practice, the counterfactual technique is further performed to tackle the insufficient training issue during optimization. Plentiful experiments on the crafted and real-world domains support the consistent effectiveness of the proposed method

Comparative Study Reveals Insights of Sheepgrass (Leymus chinensis) Coping With Phosphate-Deprived Stress Condition

Sheepgrass [Leymus chinensis (Trin.) Tzvel] is a valuable forage plant highly significant to the grassland productivity of Euro-Asia steppes. Growth of above-ground tissues of L. chinensis is the major component contributing to the grass yield. Although it is generally known that this species is sensitive to ecosystem disturbance and adverse environments, detailed information of how L. chinensis coping with various nutrient deficiency especially phosphate deprivation (-Pi) is still limited. Here, we investigated impact of Pi-deprivation on shoot growth and biomass accumulation as well as photosynthetic properties of L. chinensis. Growth inhibition of Pi-deprived seedlings was most obvious and reduction of biomass accumulation and net photosynthetic rate (Pn) was 55.3 and 63.3%, respectively, compared to the control plants grown under Pi-repleted condition. Also, we compared these characters with seedlings subjected to low-Pi stress condition. Pi-deprivation caused 18.5 and 12.3% more reduction of biomass and Pn relative to low-Pi-stressed seedlings, respectively. Further analysis of in vivo chlorophyll fluorescence and thylakoid membrane protein complexes using 2D-BN/SDS-PAGE combined with immunoblot detection demonstrated that among the measured photosynthetic parameters, decrease of ATP synthase activity was most pronounced in Pi-deprived plants. Together with less extent of lipid peroxidation of the thylakoid membranes and increased ROS scavenger enzyme activities in the leaves of Pi-deprived seedlings, we suggest that the decreased activity of ATP synthase in their thylakoids is the major cause of the greater reduction of photosynthetic efficiency than that of low-Pi stressed plants, leading to the least shoot growth and biomass production in L. chinensis

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enables the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as optimized automation via outsourced cloud analytics and increased building-grid integrations. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber-secure resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. The impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber-secure resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.Comment: 38 pages, 7 figures, 6 tables, submitted to Annual Reviews in Contro