No abstract

Abstract:Support for extremist entities -whether from the far right, or far left --often manages to survive globally online despite significant external pressure, and may ultimately inspire violent acts by individuals having no obvious prior history of extremism. Examining longitudinal records of extremist online activity, we uncovered an ecology evolving on a daily timescale that drives online support, and we provide a mathematical theory that describes it. The ecology features self-organized aggregates (online groups such as on Facebook or another social media analog) that proliferate preceding the onset of recent real-world campaigns, and adopt novel adaptive mechanisms to enhance their survival. One of the predictions is that development of large, potentially potent online groups can be thwarted by targeting smaller ones.2 Extremist entities such as ISIS (known as Islamic State) stand to benefit from the global reach and speed of the Internet for propaganda and recruiting purposes, in ways that were unthinkable for their predecessors (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). This increased connectivity may not only facilitate the formation of realworld organized groups that subsequently carry out violent attacks (e.g. the ISIS-directed attacks in Paris, November 2015) but may also inspire self-radicalized actors with no known prior history of extremism or links to extremist leadership, to operate without actually belonging to a group (e.g. the ISIS-inspired attack in San Bernardino, December 2015) (11). Recent research has used records of attacks to help elucidate group structure in past organizations for which the Internet was not a key component (3,6,12), the nature of attacks by lone-wolf actors (13) and the relationship between general online buzz and real-world events (14-16). Online buzz created by individuals that casually mention ISIS or protests is insufficient to identify any long-term build up ahead of sudden real-world events (see for example Fig. S1). This leaves open the question of how support for an entity like ISIS develops online prior to any real-world group necessarily being formed, or any real-world attack perpetrated --whether by 'recruits' or those simply 'inspired'.Our datasets consist of detailed second-by-second longitudinal records of online support activity for ISIS from its 2014 development onwards and, for comparison, online civil protestors across multiple countries within the past three years following the U.S. Open Source Indicator (OSI) project (14-16). The online Supplementary Material (SM) provides a roadmap for the paper, data descriptions and downloads. The data shows that operational pro-ISIS and protest narratives develop through selforganized online aggregates, each of which is an ad hoc group of followers of an online page created through Facebook or its global equivalents such as ВКонтакте (VKontakte) at www.vk.com (Fig. 1). These generic web-based interfaces allow such aggregates to form in a language-agnostic way, and with freely chosen names that help attract followers wi...

No abstract

Antibiotic-resistant bacteria rapidly spread in clinical and natural environments and challenge our modern lifestyle. A major component of defense against antibiotics in Gram-negative bacteria is a drug permeation barrier created by active efflux across the outer membrane. We identified molecular determinants defining the propensity of small peptidomimetic molecules to avoid and inhibit efflux pumps in Pseudomonas aeruginosa, a human pathogen notorious for its antibiotic resistance. Combining experimental and computational protocols, we mapped the fate of the compounds from structure-activity relationships through their dynamic behavior in solution, permeation across both the inner and outer membranes, and interaction with MexB, the major efflux transporter of P. aeruginosa. We identified predictors of efflux avoidance and inhibition and demonstrated their power by using a library of traditional antibiotics and compound series and by generating new inhibitors of MexB. The identified predictors will enable the discovery and optimization of antibacterial agents suitable for treatment of P. aeruginosa infections. IMPORTANCE Efflux pump avoidance and inhibition are desired properties for the optimization of antibacterial activities against Gram-negative bacteria. However, molecular and physicochemical interactions defining the interface between compounds and efflux pumps remain poorly understood. We identified properties that correlate with efflux avoidance and inhibition, are predictive of similar features in structurally diverse compounds, and allow researchers to distinguish between efflux substrates, inhibitors, and avoiders in P. aeruginosa. The developed predictive models are based on the descriptors representative of different clusters comprising a physically intuitive combination of properties. Molecular shape (represented by acylindricity), amphiphilicity (anisotropic polarizability), aromaticity (number of aromatic rings), and the partition coefficient (LogD) are physicochemical predictors of efflux inhibitors, whereas interactions with Pro668 and Leu674 residues of MexB distinguish between inhibitors/substrates and efflux avoiders. The predictive models and efflux rules are applicable to compounds with unrelated chemical scaffolds and pave the way for development of compounds with the desired efflux interface properties.

Many high-profile societal problems involve an individual or group repeatedly attacking another – from child-parent disputes, sexual violence against women, civil unrest, violent conflicts and acts of terror, to current cyber-attacks on national infrastructure and ultrafast cyber-trades attacking stockholders. There is an urgent need to quantify the likely severity and timing of such future acts, shed light on likely perpetrators, and identify intervention strategies. Here we present a combined analysis of multiple datasets across all these domains which account for >100,000 events, and show that a simple mathematical law can benchmark them all. We derive this benchmark and interpret it, using a minimal mechanistic model grounded by state-of-the-art fieldwork. Our findings provide quantitative predictions concerning future attacks; a tool to help detect common perpetrators and abnormal behaviors; insight into the trajectory of a ‘lone wolf'; identification of a critical threshold for spreading a message or idea among perpetrators; an intervention strategy to erode the most lethal clusters; and more broadly, a quantitative starting point for cross-disciplinary theorizing about human aggression at the individual and group level, in both real and online worlds.