A Potential New Pathway for Staphylococcus aureus Dissemination: The Silent Survival of S. aureus Phagocytosed by Human Monocyte-Derived Macrophages

Although considered to be an extracellular pathogen, Staphylococcus aureus is able to invade a variety of mammalian, non-professional phagocytes and can also survive engulfment by professional phagocytes such as neutrophils and monocytes. In both of these cell types S. aureus promptly escapes from the endosomes/phagosomes and proliferates within the cytoplasm, which quickly leads to host cell death. In this report we show that S. aureus interacted with human monocyte-derived macrophages in a very different way to those of other mammalian cells. Upon phagocytosis by macrophages, S. aureus persisted intracellularly in vacuoles for 3–4 days before escaping into the cytoplasm and causing host cell lysis. Until the point of host cell lysis the infected macrophages showed no signs of apoptosis or necrosis and were functional. They were able to eliminate intracellular staphylococci if prestimulated with interferon-γ at concentrations equivalent to human therapeutic doses. S. aureus survival was dependent on the alternative sigma factor B as well as the global regulator agr, but not SarA. Furthermore, isogenic mutants deficient in α-toxin, the metalloprotease aureolysin, protein A, and sortase A were efficiently killed by macrophages upon phagocytosis, although with different kinetics. In particular α-toxin was a key effector molecule that was essential for S. aureus intracellular survival in macrophages. Together, our data indicate that the ability of S. aureus to survive phagocytosis by macrophages is determined by multiple virulence factors in a way that differs considerably from its interactions with other cell types. S. aureus persists inside macrophages for several days without affecting the viability of these mobile cells which may serve as vehicles for the dissemination of infection

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Discovery of Usability Patterns in Support of Green Purchasing

Market analysis indicates that consumers are increasingly becoming more aware of the impacts of the current choices and are showing interest in understanding how to choose more healthy, ethical, and environmentally friendly items. Given the abundance of information available for this task, it has increasingly become more difficult for consumers to decipher quality among the quantity. Design and development of highly usable support tools that enable consumers to compare product selections in relation to their own individual values could greatly assist consumers in this task. There exist several usability metrics that could be utilized to determine the usability of such tools. By utilizing these metrics, system designers could obtain the necessary information to design and develop more usable support tools of this kind, thus providing consumers with the most satisfying shopping experience possible. This paper will provide an overview of work being done towards this goal. Specifically, the authors will discuss common issues relevant to the domain of environmental preferable purchasing. The authors will also discuss several metrics that could be used to determine the usability of such tools, with specific emphasis on decision accuracy. The authors hypothesize that support tools that enable consumers to obtain higher decision accuracies could provide consumers with a more satisfying shopping experience and possibly increase the selection of eco-friendly alternatives. A discussion outlining future work is also provided. 1

Evaluation of a Dominance-Based Rough Set Approach to Interface Design

This paper explores refinements to methods used in a procedure being developed by the authors to personalize user interfaces for online shopping support tools. In the authors ’ original procedure, classical methods in rough set theory are used in conjunction with traditional algorithms in web usage mining. This paper will explore an alternative approach, specifically the dominance-based rough set approach (DRSA), for use with the authors ’ original procedure. DRSA has its foundations in the classical rough set approach (CRSA). However unlike CRSA, DRSA considers feature/preference-ordered data. In web usage mining analyses, where elicitation of user preferences is a common task, feature/preference order is an important factor and may provide insights that classical/traditional approaches may omit. The authors discuss how DRSA may benefit and improve their original procedure and discuss how the information gained from DRSA analyses could be used to further build their original procedure by enabling item ordering and feature highlighting. This paper will describe the research process, outcomes, and outline opportunities for future work.