Determination of in silico rules for predicting small molecule binding behavior to nucleic acids in vitro.

The vast knowledge of nucleic acids is evolving and it is now known that DNA can adopt highly complex, heterogeneous structures. Among the most intriguing are the G-quadruplex structures, which are thought to play a pivotal role in cancer pathogenesis. Efforts to find new small molecules for these and other physiologically relevant nucleic acid structures have generally been limited to isolation from natural sources or rationale synthesis of promising lead compounds. However, with the rapid growth in computational power that is increasingly becoming available, virtual screening and computational approaches are quickly becoming a reality in academia and industry as an efficient and economical way to discover new lead compounds. These computational efforts have historically almost entirely focused on proteins as targets and have neglected DNA. We present research here showing that not only can software be utilized for targeting DNA, but that selectivity metrics can be developed to predict the binding mechanism of a small molecule to a DNA target. The software Surflex and Autodock were chosen for evaluation and were demonstrated to be able to accurately reproduce the known crystal structures of several small molecules that bind by the most common nucleic acid interacting mechanisms of groove binding and intercalation. These software were further used to rationalize known affinity and selectivity data of a 67 compound library of compounds for a library of nucleic acid structures including duplex, triplex and quadruplexes. Based upon the known binding behavior of these compounds, in silica metrics were developed to classify compounds as either groove binders or intercalators. These rules were subsequently used to identify new triplex and quadruplex binding small molecules by structure and ligand-based virtual screening approaches using a virtual library consisting of millions of commercially available small molecules. The binding behavior of the newly discovered triplex and quadruplex binding compounds was empirically validated using a number of spectroscopic, fluorescent and thermodynamic equilibrium techniques. In total, this research predicted the binding behavior of these test compounds in silica and subsequently validated these findings in vitro. This research presents a novel approach to discover lead compounds that target multiple nucleic acid morphologies

The long-run performance of U.S. bidding firms in the post M&A period : the impact of bid type, payment method and industry specialisation

This study investigates how mergers and acquisitions (M&A) affect the wealth of shareholders of public firms in the United States (U.S). More specifically, it investigates whether the nature of the bid, the payment method used, and the type of M&A have implications for shareholders of U.S bidding firms. The study analyses 352 mergers and acquisitions in the U.S during the period 1999-2008, and its results indicate that bidding firms suffer significant negative buy-and-hold abnormal returns in the three years period after a M&A announcement. The results also suggest that, in the long-run, hostile bids and cash-financed bidders outperform friendly bids and stock-funded bidders, respectively. Furthermore, the study also finds that in the long-run bidder firms that focus on industry specialisation within their M&A targets significantly outperform firms that adopt a more diversified strategy. The analysis also investigates the effects of M&A specialisation/diversification in six different sectors, and finds that specialised bidders outperform diversified bidders in four sectors: consumer & basic materials, energy & utilities, communications and technology. Furthermore, bidder firms in the financial services sector perform significantly better when diversifying into other sectors, while the performance of bidder firms in the industrial sector appears unaffected by the degree of M&A specialisation or diversification

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent “omic”-level findings, and examine how these findings have been systematically integrated to generate further insight. Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or “endotypes” that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic, and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma. The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.This work was supported by the National Health and Medical Research Council (NHMRC) of Australia via a postgraduate scholarship (ref. no. 1114753) to HHF Tang, research grant (1049539) to M Inouye and K Holt, and Fellowships (1061409) to K Holt and (1061435) to M Inouye. K Holt was further supported by a Senior Medical Research Fellowship from the Viertel Foundation of Australia

The Information Society in Science Fiction

This paper examines the range of ways that science fiction literature, film, and television programming have portrayed aspects of concept of the information society. It introduces both science fiction and the information society using contemporary scholarship. It goes on to examine aspects of the information society along political, economic, and cultural axes and then explores treatments in relevant works. The paper concludes that the science fiction genre has much to offer scholars and students of information society studies, and it proposes the incorporation of science fiction into information science curriculum and canon as a narrative parallel to standard non-narrative scholarship

Enabling Parametric Optimal Ascent Trajectory Modeling During Early Phases of Design

During the early phases of engineering design, the costs committed are high, costs incurred are low, and the design freedom is high. It is well documented that decisions made in these early design phases drive the entire design's life cycle. In a traditional paradigm, key design decisions are made when little is known about the design. As the design matures, design changes become more difficult -- in both cost and schedule -- to enact. Indeed, the current capability-based paradigm that has emerged because of the constrained economic environment calls for the infusion of knowledge acquired during later design phases into earlier design phases, i.e. bring knowledge acquired during preliminary and detailed design into pre-conceptual and conceptual design. An area of critical importance to launch vehicle design is the optimization of its ascent trajectory, as the optimal trajectory will be able to take full advantage of the launch vehicle's capability to deliver a maximum amount of payload into orbit. Hence, the optimal ascent trajectory plays an important role in the vehicle's affordability posture as the need for more economically viable access to space solutions are needed in today's constrained economic environment. The problem of ascent trajectory optimization is not a new one. There are several programs that are widely used in industry that allows trajectory analysts to, based on detailed vehicle and insertion orbit parameters, determine the optimal ascent trajectory. Yet, little information is known about the launch vehicle early in the design phase - information that is required of many different disciplines in order to successfully optimize the ascent trajectory. Thus, the current paradigm of optimizing ascent trajectories involves generating point solutions for every change in a vehicle's design parameters. This is often a very tedious, manual, and time-consuming task for the analysts. Moreover, the trajectory design space is highly non-linear and multi-modal due to the interaction of various constraints. Additionally, when these obstacles are coupled with The Program to Optimize Simulated Trajectories [1] (POST), an industry standard program to optimize ascent trajectories that is difficult to use, it requires expert trajectory analysts to effectively optimize a vehicle's ascent trajectory. As it has been pointed out, the paradigm of trajectory optimization is still a very manual one because using modern computational resources on POST is still a challenging problem. The nuances and difficulties involved in correctly utilizing, and therefore automating, the program presents a large problem. In order to address these issues, the authors will discuss a methodology that has been developed. The methodology is two-fold: first, a set of heuristics will be introduced and discussed that were captured while working with expert analysts to replicate the current state-of-the-art; secondly, leveraging the power of modern computing to evaluate multiple trajectories simultaneously, and therefore, enable the exploration of the trajectory's design space early during the pre-conceptual and conceptual phases of design. When this methodology is coupled with design of experiments in order to train surrogate models, the authors were able to visualize the trajectory design space, enabling parametric optimal ascent trajectory information to be introduced with other pre-conceptual and conceptual design tools. The potential impact of this methodology's success would be a fully automated POST evaluation suite for the purpose of conceptual and preliminary design trade studies. This will enable engineers to characterize the ascent trajectory's sensitivity to design changes in an arbitrary number of dimensions and for finding settings for trajectory specific variables, which result in optimal performance for a "dialed-in" launch vehicle design. The effort described in this paper was developed for the Advanced Concepts Office [2] at NASA Marshall Space Flight Cente

Differential gene network analysis for the identification of asthma-associated therapeutic targets in allergen-specific T-helper memory responses

Fifty most significant differentially expressed genes in HDM-stimulated versus resting CD4 T cells from HDM-sensitized atopics with asthmatics. Gene expression patterns were compared between HDM-stimulated and unstimulated CD4 T cells from HDM-sensitized atopics with asthma. Here we present the 50 most significant differentially expressed genes. (XLS 34 kb

Soothing signals: transplacental transmission of resistance to asthma and allergy

The progressive rise in the prevalence of allergic diseases since the 1970s is widely attributed to diminished exposure to microbial stimuli, resulting in dysregulated immune functions during early life. Most studies investigating the mechanism behind this phenomenon have focused on postnatal microbial exposure. But emerging evidence suggests that such programming may also occur in the developing fetus as a result of microbial stimulation of the pregnant mother