Stilbenes: Therapeutic Interventions Targeting Amyloid β Protein Aggregation In Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia and accounts for 60-80 % of all dementia cases. In the United States, AD is ranked as the 6th leading cause of death and it is the only one among the top 10 that cannot be prevented, treated or even slowed. All FDA approved drugs focus on attenuating the symptoms for a limited time by regulating neurotransmitters without any intervention with the underlying disease process. A major player in the initiation and development of this debilitating disorder is the misfolding and subsequent aggregating of amyloid-β (Aβ) peptide. This aggregation process converts non-toxic functional Aβ peptides into a spectrum of neurotoxic Aβ aggregated species, such as, oligomers, soluble aggregates and fibrils. These aggregated forms are believed to be the source of inflammation and oxidative stress that cause neuron death and the loss of synaptic functions. Therefore, small molecules that could intervene with the aggregation of Aβ peptides may present an effective AD therapeutic prevention and treatment. The work presented here examined the intervening effects of eight stilbenes, such as resveratrol and piceatannol, on multiple mechanistic steps of Aβ aggregation. Results identified trans-piceatannol and cis-piceatannol as the most potent compounds that could affect distinct aggregation pathways, specifically, modifying the conformation of Aβ aggregates, as well as, reducing soluble aggregate growth rate. The pronounced change in Aβ aggregate conformation might be related to a change in cytotoxicity. Their superior effects could be attributed to the presence of catechol moiety. The change in Aβ aggregate conformation induced by trans-piceatannol substantially reduced the Aβ aggregates binding to lipid bilayers, which plays a vital role in the induction of neurotoxicity. Other stilbenes, including resveratrol, also altered the morphology of Aβ aggregates but they exhibited only moderate interfering effects on Aβ aggregation pathways. Together, this study provides insight into the effective interventions of piceatannol on Aβ aggregation and proposes this natural compound as a novel promising small molecule for the prevention and treatment of AD

A Multi-objective Location Decision Making Model for Emergency Shelters Giving Priority to Subjective Evaluation of Residents

Earthquake is regarded as the most destructive and terrible disaster among all-natural disasters [1]. Experts agree that immediate emergency evacuation is the safest and most effective response to the earthquake disaster [2]. In the research of emergency evacuation planning, the influence of human subjectivity has gradually attracted researchers’ attention. In this paper, we take the human subjectivity as one of the most important factors for emergency evacuation planning. Based on the preferences of the residents at each demand point for the attributes of every candidate emergency shelter, the subjective score of each candidate emergency shelter is obtained. The preferences of residents will change with the refuge time, so do the weights of residents’ subjective scores of all attributes of candidate emergency shelters. Therefore, we use the subjective score function to describe the change of residents’ evaluations for the emergency shelter over time, and take the average value of subjective scores at all refuge times as the primary basis for location decision making. On these bases, we build a multi-objective location decision making model for emergency shelters giving priority to subjective evaluation of residents. In the model, we consider transfer distance, the efficiency of construction funds and the distribution of people among emergency shelters. Considering fairness, we minimize the standard deviation of the scores and the standard deviation of the transfer distances in the model. This model is applied to a case, which verifies its feasibility and shows that human subjectivity plays an important role in emergency evacuation planning

Alleviation of cadmium toxicity in cucumber (Cucumis sativus) seedlings by the application of selenium

In the present study, the role of selenium in cadmium toxicity was investigated in cucumber seedlings by hydroponic experiments. The application of Se for cucumber exposed to Cd significantly reduced Cd accumulation in all tissues, elevated Cd-depressed chlorophyll content, and improved photosynthetic performance. External Se significantly reduced ·OH, H2O2 and malondialdehyde content. Exogenous Se balanced Cd-depressed elements (e.g., Se enhanced Cd-induced decreases in root Zn, leaf/stem/root Mn concentrations) and carbohydrate contents. External Se also significantly decreased the Cd-induced increases in Na+K+-, Ca2+Mg2+- and total ATPase activities, which recovered almost to control level. Results indicate that application of Se can alleviate Cd toxicity in cucumber seedlings by reducing Cd uptake and reactive oxygen species (ROS) accumulation, moreover protecting photosynthetic machinery from damaging, balancing elements and carbohydrate contents, and improving ATPase activities in cucumber

Multi-Objective Model to Improve Network Reliability Level under Limited Budget by Considering Selection of Facilities and Total Service Distance in Rescue Operations

Sudden disasters may damage facilities, transportation networks and other critical infrastructures, delay rescue and bring huge losses. Facility selection and reliable transportation network play an important role in emergency rescue. In this paper, the reliability level between two points in a network is defined from the point of view of minimal edge cut and path, respectively, and the equivalence of these two definitions is proven. Based on this, a multi-objective optimization model is proposed. The first goal of the model is to minimize the total service distance, and the second goal is to maximize the network reliability level. The original model is transformed into a model with three objectives, and the three objectives are combined into one objective by the method of weighting. The model is applied to a case, and the results are analyzed to verify the effectiveness of the model

Techniques for Compact Planar MIMO Antennas

MIMO Technology has promoted the developments of various antennas, then the planar antenna will be one of the main directions to satisfy the future compact requirement of the 5G+/6G communications. This chapter introduces different types of the planar antenna and summarizes the implicit compact techniques, where the related techniques like the diversity and the reconfigurable are not included owing to they are the inherent properties of the MIMO antennas. These antennas contain the patch antenna, slot antenna, dipole/monopole antenna, loop antenna, cavity antenna, Yagi-Uda antenna, fractal antenna, UWB antenna, PIFA etc., and their deformations to the specific purposes. On the contrary, the implicit compact techniques are not so explicit as the antenna configurations, but they are classified to be the close-spacing structure without decoupling, owing to the decoupling is not the necessary requirement of MIMO application, decoupling technique of spacing reduction, meandered line technique, multi-element method, co-radiator/co-location design, fractal antenna, and radiator-cutting antenna. Besides, the corresponding techniques for the compact design are also concluded, including the mode-cutting method, fractal technique, characteristic mode analysis, and the optimization algorithms

Low frequency noise in chemical vapor deposited MoS2

Inherent low frequency noise is a ubiquitous phenomenon, which limits operation and performance of electronic devices and circuits. This limiting factor is very important for nanoscale electronic devices, such as 2D semiconductor devices. In this work, low frequency noise in high mobility single crystal MoS2 grown by chemical vapor deposition (CVD) is investigated. The measured low frequency noise follows an empirical formulation of mobility fluctuations with Hooge' s parameter ranging between 1.44E-3 and 3.51E-2. Small variation of Hooge's parameter suggests superior material uniformity and processing control of CVD grown MoS2 devices than reported single-layer MoS2 FET. The extracted Hooge's parameter is one order of magnitude lower than CVD grown graphene. The Hooge's parameter shows an inverse relationship with the field mobility

The Strip-Ground Rectangular Patch Antenna

To imitate the broken situation of the conductive threads of the wearable antenna, a strip-ground design of rectangular patch antenna with the conventional substrate is presented to investigate the change of the antenna performance, where the ground is sliced along its E-plane. The strips are symmetrical along the center line of the width of the patch, and the gap ratio of the gap to the solid ground varies with the change of gaps. The conventional patch antenna is used as a reference for comparison with the stripe-ground antennas. And the effect of the coaxial cable, the changes of the impedance, and the cross-polarization of the antenna are investigated. Several antenna prototypes are fabricated and their measured results are in good agreement with the simulations. These results show that the gaps change the performance of the strip-ground antennas, but they can work well if the gap ratio is less than 1 : 4

Online Meta-Critic Learning for Off-Policy Actor-Critic Methods

Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks. Normally, the critic's action-value function is updated using temporal-difference, and the critic in turn provides a loss for the actor that trains it to take actions with higher expected return. In this paper, we introduce a novel and flexible meta-critic that observes the learning process and meta-learns an additional loss for the actor that accelerates and improves actor-critic learning. Compared to the vanilla critic, the meta-critic network is explicitly trained to accelerate the learning process; and compared to existing meta-learning algorithms, meta-critic is rapidly learned online for a single task, rather than slowly over a family of tasks. Crucially, our meta-critic framework is designed for off-policy based learners, which currently provide state-of-the-art reinforcement learning sample efficiency. We demonstrate that online meta-critic learning leads to improvements in avariety of continuous control environments when combined with contemporary Off-PAC methods DDPG, TD3 and the state-of-the-art SAC.Comment: NeurIPS 202