Diverse small molecule inhibitors of human apurinic/apyrimidinic endonuclease APE1 identified from a screen of a large public collection.

The major human apurinic/apyrimidinic endonuclease APE1 plays a pivotal role in the repair of base damage via participation in the DNA base excision repair (BER) pathway. Increased activity of APE1, often observed in tumor cells, is thought to contribute to resistance to various anticancer drugs, whereas down-regulation of APE1 sensitizes cells to DNA damaging agents. Thus, inhibiting APE1 repair endonuclease function in cancer cells is considered a promising strategy to overcome therapeutic agent resistance. Despite ongoing efforts, inhibitors of APE1 with adequate drug-like properties have yet to be discovered. Using a kinetic fluorescence assay, we conducted a fully-automated high-throughput screen (HTS) of the NIH Molecular Libraries Small Molecule Repository (MLSMR), as well as additional public collections, with each compound tested as a 7-concentration series in a 4 µL reaction volume. Actives identified from the screen were subjected to a panel of confirmatory and counterscreen tests. Several active molecules were identified that inhibited APE1 in two independent assay formats and exhibited potentiation of the genotoxic effect of methyl methanesulfonate with a concomitant increase in AP sites, a hallmark of intracellular APE1 inhibition; a number of these chemotypes could be good starting points for further medicinal chemistry optimization. To our knowledge, this represents the largest-scale HTS to identify inhibitors of APE1, and provides a key first step in the development of novel agents targeting BER for cancer treatment

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

An Integrated Approach for Characterizing Aerosol Climate Impacts and Environmental Interactions

Aerosols exert myriad influences on the earth's environment and climate, and on human health. The complexity of aerosol-related processes requires that information gathered to improve our understanding of climate change must originate from multiple sources, and that effective strategies for data integration need to be established. While a vast array of observed and modeled data are becoming available, the aerosol research community currently lacks the necessary tools and infrastructure to reap maximum scientific benefit from these data. Spatial and temporal sampling differences among a diverse set of sensors, nonuniform data qualities, aerosol mesoscale variabilities, and difficulties in separating cloud effects are some of the challenges that need to be addressed. Maximizing the long-term benefit from these data also requires maintaining consistently well-understood accuracies as measurement approaches evolve and improve. Achieving a comprehensive understanding of how aerosol physical, chemical, and radiative processes impact the earth system can be achieved only through a multidisciplinary, inter-agency, and international initiative capable of dealing with these issues. A systematic approach, capitalizing on modern measurement and modeling techniques, geospatial statistics methodologies, and high-performance information technologies, can provide the necessary machinery to support this objective. We outline a framework for integrating and interpreting observations and models, and establishing an accurate, consistent, and cohesive long-term record, following a strategy whereby information and tools of progressively greater sophistication are incorporated as problems of increasing complexity are tackled. This concept is named the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON). To encompass the breadth of the effort required, we present a set of recommendations dealing with data interoperability; measurement and model integration; multisensor synergy; data summarization and mining; model evaluation; calibration and validation; augmentation of surface and in situ measurements; advances in passive and active remote sensing; and design of satellite missions. Without an initiative of this nature, the scientific and policy communities will continue to struggle with understanding the quantitative impact of complex aerosol processes on regional and global climate change and air quality

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

The impact and penetration of location-based services

Since the invention of digital technology, its development has followed an entrenched path ofminiaturisation and decentralisation with increasing focus on individual and niche applications. Computerhardware has moved from remote centres to desktop and hand held devices whilst being embedded invarious material infrastructures. Software has followed the same course. The entire process has convergedon a path where various analogue devices have become digital and are increasingly being embedded inmachines at the smallest scale. In a parallel but essential development, there has been a convergence ofcomputers with communications ensuring that the delivery and interaction mechanisms for computersoftware is now focused on networks of individuals, not simply through the desktop, but in mobilecontexts. Various inert media such as fixed television is becoming more flexible as computers and visualmedia are becoming one.With such massive convergence and miniaturisation, new software and new applications define the cuttingedge. As computers are being increasingly tailored to individual niches, then new digital services areemerging, many of which represent applications which hitherto did not exist or at best were rarely focusedon a mass market. Location based services form one such application and in this paper, we will bothspeculate on and make some initial predictions of the geographical extent to which such services willpenetrate different markets. We define such services in detail below but suffice it to say at this stage thatsuch functions involve the delivery of traditional services using digital media and telecommunications.High profile applications are now being focused on hand held devices, typically involving information onproduct location and entertainment but wider applications involve fixed installations on the desktop whereservices are delivered through traditional fixed infrastructure. Both wire and wireless applications definethis domain. The market for such services is inevitably volatile and unpredictable at this early stage but wewill attempt here to provide some rudimentary estimates of what might happen in the next five to tenyears.The ?network society? which has developed through this convergence, is, according to Castells (1989,2000) changing and re-structuring the material basis of society such that information has come todominate wealth creation in a way that information is both a raw material of production and an outcome ofproduction as a tradable commodity. This has been fuelled by the way technology has expanded followingMoore?s Law and by fundamental changes in the way telecommunications, finance, insurance, utilitiesand so on is being regulated. Location based services are becoming an integral part of this fabric and thesereflect yet another convergence between geographic information systems, global positioning systems, andsatellite remote sensing. The first geographical information system, CGIS, was developed as part of theCanada Land Inventory in 1965 and the acronym ?GIS? was introduced in 1970. 1971 saw the firstcommercial satellite, LANDSAT-1. The 1970s also saw prototypes of ISDN and mobile telephone and theintroduction of TCP/IP as the dominant network protocol. The 1980s saw the IBM XT (1982) and thebeginning of de-regulation in the US, Europe and Japan of key sectors within the economy. Finally in the 1990s, we saw the introduction of the World Wide Web and the ubiquitous pervasion of business andrecreation of networked PC?s, the Internet, mobile communications and the growing use of GPS forlocational positioning and GIS for the organisation and visualisation of spatial data. By the end of the 20thcentury, the number of mobile telephone users had reached 700 million worldwide. The increasingmobility of individuals, the anticipated availability of broadband communications for mobile devices andthe growing volumes of location specific information available in databases will inevitably lead to thedemand for services that will deliver location related information to individuals on the move. Suchlocation based services (LBS) although in a very early stage of development, are likely to play anincreasingly important part in the development of social structures and business in the coming decades.In this paper we begin by defining location based services within the context we have just sketched. Wethen develop a simple model of the market for location-based services developing the standard non-linearsaturation model of market penetration. We illustrate this for mobile devices, namely mobile phones in thefollowing sections and then we develop an analysis of different geographical regimes which arecharacterised by different growth rates and income levels worldwide. This leads us to speculate on theextent to which location based services are beginning to take off and penetrate the market. We concludewith scenarios for future growth through the analogy of GIS and mobile penetration