BackgroundBased on the pharmacological potency and structural features of succinimides, this study was designed to synthesize new ketoesters derivatives of succinimides. Furthermore, the synthesized compounds were evaluated for their possible anticholinesterase and antioxidant potentials. The compounds were synthesized by organocatalytic Michael additions of α-ketoesters to N-aryl maleimides. Acetyl and butyrylcholinesterase inhibitory activities were determined using Ellman’s spectrophotometric assay. The antioxidant activity was performed with DPPH and ABTS free radicals scavenging assay.ResultsThe Michael additions of α-ketoesters to maleimides was promoted by 8-hydroxyquinoline. The organocatalyst (8-hydroxyquinoline, 20 mol %) produced the compounds in relatively shorter time (20–24 h) and with excellent isolated yields (84-98 %). The synthesized compounds (1–4) showed outstanding acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory potentials, i.e., 98.75 and 90.00 % respectively for compound 2, with IC50 < 0.1 μg/mL. Additionally, compounds 1–4 revealed moderate antioxidant activity at different concentrations. In DPPH free radical scavenging assay, compound 1 showed dominant result with 72.41 ± 0.45, 52.49 ± 0.78 and 35.60 ± 0.75 % inhibition at concentrations of 1000, 500 and 250 μg/mL respectively, IC50 value of 440 μg/mL. However, the free radical scavenging was better when used ABTS free radicals. In ABTS free radicals scavenging assay compound 1 exhibited 88.51 ± 0.62 % inhibition at highest tested concentration i.e., 1000 μg/mL.ConclusionsHerein, we have synthesized four ketoesters derivatives of succinimides in a single step reaction and high yields. As a highlight, we have showed a first report on the anticholinesterase and antioxidant potentials of succinimides. All the compounds showed overwhelming enzyme inhibitions and moderate antioxidant potentials.Graphical AbstractGraphical representation of synthesis, anticholinesterase and antioxidant potentials of ketoester derivatives of succinimides.Electronic supplementary materialThe online version of this article (doi:10.1186/s13065-015-0107-2) contains supplementary material, which is available to authorized users.
Spatial statistical techniques can be an effective tool for analyzing patterns and autocorrelation in crash data, especially weather-related crashes. Since weather is a geographic phenomenon, it tends to show distinct geographic patterns affecting certain locations more than others. Accordingly, "weather-related" crashes may also display similar distinct patterns or clustering. The objective of this research was to use spatial statistical techniques to identify significant patterns of weather-related crashes. Weather-related crashes, defined as those crashes which occurred in adverse weather conditions, were analyzed using the Getis-Ord G i * ͑d͒ statistic. The statistic reveals spatial patterns for weather-related crashes which are clustered at different locations depending upon weather conditions ͑snow, rain, and fog͒. The results also show geographic areas ͑counties͒ of statistically significant high and low relative crash rates for each weather condition. Furthermore, the resulting patterns of crashes were validated by comparing counties of high and low crash rates with areas of varying weather data. The establishment of this relationship between weather and crashes is imperative in identifying the variables contributing to these crash types and the implementation of effective countermeasures for road weather safety audit purposes.
The objective of this research was to develop prediction models for total crashes and fatal or injury crashes for rural horizontal curves on undivided roads, with a focus on three distinct aspects. The first was an emphasis on assembling a large, high-quality data set. Crash prediction models were developed by using a data set of 11,427 rural horizontal curves on Wisconsin state trunk network roads with more than 13 parameters and four distinct types of crash data sets. The second focus area was to use regression tree analysis in creating a simple model of horizontal curve safety aimed at practitioners of systemic road safety management and creating subsets of data that warranted further analysis. Regression tree results identified the curve radius of approximately 2,500 ft as a significant point below which there is a marked increase in crashes on horizontal curves. The third focus area was to research the effect on horizontal curve crash prediction models of different selection criteria to assemble the crash data set. Models (total and fatal or injury) based on a crash data set with and without crashes in the proximity of intersections were compared. The results show that when crashes on horizontal curves are selected where crash report forms indicate the presence of a horizontal curve, crashes that occur in the proximity of intersections do not affect model results significantly; therefore, the inclusion of such crashes would increase the size of the data set and benefit model development.
Accidents on horizontal curves cause a significant amount of pain and suffering to those involved in the accidents because of the nature of the collisions. Curves are necessary and important elements of every highway. Implementing strategies designed to improve the safety of horizontal curves will help achieve goal of highway safety. The goal of the strategic highway safety is to reduce annual highway fatalities. This goal can be achieved through the widespread application of low-cost, proven countermeasures that reduce the number of crashes on the highways. This paper provides an overall framework for coordinating a safety program. It will be of particular interest to safety practitioners with responsibility for reduce injuries and fatalities on the highway system.
The availability and quality of transportation data is a cornerstone of any data-driven program. There is a continuous need to identify and develop alternative, reliable, and inexpensive sources of data and efficient and robust integration techniques. This research presents an innovative cost-effective application to collect geographic information system (GIS)–compatible data from image-based databases. Road inventory data on guardrail end-type locations along with other road features on more than 8,000 mi of Wisconsin State Trunk Network highways were collected. Data collected from image-based sources with Global Positioning System coordinates presented the familiar problem of spatial mismatch. A framework was developed based on the principles of dynamic segmentation to integrate the data and resolve the spatial mismatch problem. The principles of dynamic segmentation and route calibration are well established in literature. However, there were no specific examples of a framework that created a workable program and addressed issues pertaining to practical solutions for statewide data. The framework developed presents an efficient and automated solution for data integration, which is applicable to any relevant data set. A quantitative assessment of the performance of the data collection and map-matching procedures was conducted to assess the results. The results showed that road features collected from the image-based data sets were located within an average distance of 6 to 7 m of their location on the Wisconsin Department of Transportation GIS base maps, which were highly accurate, given the limitations of the data sets.
The modern roundabouts are proliferating rapidly in the United States and Wisconsin is no exception to this trend. The growing number of U.S.-specific research has played an important role in their acceptance in the United States. However, as new data become available, there is a need to continue the research to better understand roundabout safety in the United States. Moreover, the growing data sets also warrant the creation of localized models to better reflect ground conditions. The objectives of this research were to continue and enhance research efforts on the roundabout safety using current data sets. The aim was to analyze roundabout crash trend and patterns to further evaluate their performance under varying situations and develop crash prediction models. The results showed interesting observations as far as crash patterns at roundabouts were concerned. Even though crash severity was reduced, it is not the same situation for crash frequencies. Further research is required to assess the safety effectiveness of roundabouts in Wisconsin. The crash prediction models from this research would help in quantifying roundabout safety, especially when selecting which locations to be converted to roundabouts.
Advances in geographic information system (GIS) software and exploratory spatial data analysis (ESDA) techniques give transportation safety engineers tools to observe and analyze safety-related data from a new perspective. This research takes the use of GIS software and ESDA techniques one step further by incorporating advanced statistical techniques for a more thorough and complex analysis of safety data. This is achieved by implementing a network-constrained cross K-function to analyze the relationship between bridges and the occurrences of ice-related crashes within a county. The counties in Wisconsin included in the analysis were selected through the use of the local Moran's I statistic; this statistic allows for the selection of counties within the same geographical area, which have similar parameters (in this case, ice-related crash rates). The objective of this research is to explore the relationship between ice-related crashes and bridges in counties that display similar ice-related crash rates, to compare and analyze winter maintenance techniques. The results identify clustering of ice-related crashes around bridges in four counties with similar ice-related crash rates in southeast Wisconsin. Similarly, two of four counties show clustering of ice-related crashes around bridges in northwest Wisconsin. These results make a strong case to suggest that counties in these regions should focus additional winter maintenance efforts at bridge locations. In addition, this research shows how the use of advanced spatial statistical techniques, particularly network-based statistics applied within a GIS environment, can be used as a unique and innovative approach toward safety data analysis.
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