Synthesis, biological evaluation, molecular docking, and MD simulation of novel 2,4-disubstituted quinazoline derivatives as selective butyrylcholinesterase inhibitors and antioxidant agents.

Alzheimer's disease is the most prevalent neurodegenerative disorder characterized by significant memory loss and cognitive impairments. Studies have shown that the expression level and activity of the butyrylcholinesterase enzyme increases significantly in the late stages of Alzheimer's disease, so butyrylcholinesterase can be considered as a promising therapeutic target for potential Alzheimer's treatments. In the present study, a novel series of 2,4-disubstituted quinazoline derivatives (6a-j) were synthesized and evaluated for their inhibitory activities against acetylcholinesterase (AChE) and butyrylcholinestrase (BuChE) enzymes, as well as for their antioxidant activities. The biological evaluation revealed that compounds 6f, 6h, and 6j showed potent inhibitory activities against eqBuChE, with IC50 values of 0.52, 6.74, and 3.65 µM, respectively. These potent compounds showed high selectivity for eqBuChE over eelAChE. The kinetic study demonstrated a mixed-type inhibition pattern for both enzymes, which revealed that the potent compounds might be able to bind to both the catalytic active site and peripheral anionic site of eelAChE and eqBuChE. In addition, molecular docking studies and molecular dynamic simulations indicated that potent compounds have favorable interactions with the active sites of BuChE. The antioxidant screening showed that compounds 6b, 6c, and 6j displayed superior scavenging capabilities compared to the other compounds. The obtained results suggest that compounds 6f, 6h, and 6j are promising lead compounds for the further development of new potent and selective BuChE inhibitors.

An uncertainty-based framework to quantifying climate change impacts on coastal flood vulnerability: case study of New York City.

The continued development efforts around the world, growing population, and the increased probability of occurrence of extreme hydrologic events have adversely affected natural and built environments. Flood damages and loss of lives from the devastating storms, such as Irene and Sandy on the East Coast of the USA, are examples of the vulnerability to flooding that even developed countries have to face. The odds of coastal flooding disasters have been increased due to accelerated sea level rise, climate change impacts, and communities' interest to live near the coastlines. Climate change, for instance, is becoming a major threat to sustainable development because of its adverse impacts on the hydrologic cycle. Effective management strategies are thus required for flood vulnerability reduction and disaster preparedness. This paper is an extension to the flood resilience studies in the New York City coastal watershed. Here, a framework is proposed to quantify coastal flood vulnerability while accounting for climate change impacts. To do so, a multi-criteria decision making (MCDM) approach that combines watershed characteristics (factors) and their weights is proposed to quantify flood vulnerability. Among the watershed characteristics, potential variation in the hydrologic factors under climate change impacts is modeled utilizing the general circulation models' (GCMs) outputs. The considered factors include rainfall, extreme water level, and sea level rise that exacerbate flood vulnerability through increasing exposure and susceptibility to flooding. Uncertainty in the weights as well as values of factors is incorporated in the analysis using the Monte Carlo (MC) sampling method by selecting the best-fitted distributions to the parameters with random nature. A number of low impact development (LID) measures are then proposed to improve watershed adaptive capacity to deal with coastal flooding. Potential range of current and future vulnerability to flooding is estimated with and without consideration of climate change impacts and after implementation of LIDs. Results show that climate change has the potential to increase rainfall intensity, flood volume, floodplain extent, and flood depth in the watershed. The results also reveal that improving system resilience by reinforcing the adaptation capacity through implementing LIDs could mitigate flood vulnerability. Moreover, the results indicate the significant effect of uncertainties, arising from the factors' weights as well as climate change, impacts modeling approach, on quantifying flood vulnerability. This study underlines the importance of developing applicable schemes to quantify coastal flood vulnerability for evolving future responses to adverse impacts of climate change.

A multi-stakeholder framework for urban runoff quality management: Application of social choice and bargaining techniques.

In this paper, an integrated framework is proposed for urban runoff management. To control and improve runoff quality and quantity, Low Impact Development (LID) practices are utilized. In order to determine the LIDs' areas and locations, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which considers three objective functions of minimizing runoff volume, runoff pollution and implementation cost of LIDs, is utilized. In this framework, the Storm Water Management Model (SWMM) is used for stream flow simulation. The non-dominated solutions provided by the NSGA-II are considered as management scenarios. To select the most preferred scenario, interactions among the main stakeholders in the study area with conflicting utilities are incorporated by utilizing bargaining models including a non-cooperative game, Nash model and social choice procedures of Borda count and approval voting. Moreover, a new social choice procedure, named pairwise voting method, is proposed and applied. Based on each conflict resolution approach, a scenario is identified as the ideal solution providing the LIDs' areas, locations and implementation cost. The proposed framework is applied for urban water quality and quantity management in the northern part of Tehran metropolitan city, Iran. Results show that the proposed pairwise voting method tends to select a scenario with a higher percentage of reduction in TSS (Total Suspended Solid) load and runoff volume, in comparison with the Borda count and approval voting methods. Besides, the Nash method presents a management scenario with the highest cost for LIDs' implementation and the maximum values for percentage of runoff volume reduction and TSS removal. The results also signify that selection of an appropriate management scenario by stakeholders in the study area depends on the available financial resources and the relative importance of runoff quality improvement in comparison with reducing the runoff volume.