Information flow reveals prediction limits in online social activity.

Modern society depends on the flow of information over online social networks, and users of popular platforms generate substantial behavioural data about themselves and their social ties1-5. However, it remains unclear what fundamental limits exist when using these data to predict the activities and interests of individuals, and to what accuracy such predictions can be made using an individual's social ties. Here, we show that 95% of the potential predictive accuracy for an individual is achievable using their social ties only, without requiring that individual's data. We used information theoretic tools to estimate the predictive information in the writings of Twitter users, providing an upper bound on the available predictive information that holds for any predictive or machine learning methods. As few as 8-9 of an individual's contacts are sufficient to obtain predictability compared with that of the individual alone. Distinct temporal and social effects are visible by measuring information flow along social ties, allowing us to better study the dynamics of online activity. Our results have distinct privacy implications: information is so strongly embedded in a social network that, in principle, one can profile an individual from their available social ties even when the individual forgoes the platform completely.

Publisher Correction: Information flow reveals prediction limits in online social activity.

The original and corrected figures are shown in the accompanying Publisher Correction.

Selection and characterization of alanine racemase inhibitors against Aeromonas hydrophila.

BACKGROUND: Combining experimental and computational screening methods has been of keen interest in drug discovery. In the present study, we developed an efficient screening method that has been used to screen 2100 small-molecule compounds for alanine racemase Alr-2 inhibitors. RESULTS: We identified ten novel non-substrate Alr-2 inhibitors, of which patulin, homogentisic acid, and hydroquinone were active against Aeromonas hydrophila. The compounds were found to be capable of inhibiting Alr-2 to different extents with 50% inhibitory concentrations (IC50) ranging from 6.6 to 17.7 µM. These compounds inhibited the growth of A. hydrophila with minimal inhibitory concentrations (MICs) ranging from 20 to 120 µg/ml. These compounds have no activity on horseradish peroxidase and D-amino acid oxidase at a concentration of 50 µM. The MTT assay revealed that homogentisic acid and hydroquinone have minimal cytotoxicity against mammalian cells. The kinetic studies indicated a competitive inhibition of homogentisic acid against Alr-2 with an inhibition constant (K i) of 51.7 µM, while hydroquinone was a noncompetitive inhibitor with a K i of 212 µM. Molecular docking studies suggested that homogentisic acid binds to the active site of racemase, while hydroquinone lies near the active center of alanine racemase. CONCLUSIONS: Our findings suggested that combining experimental and computational methods could be used for an efficient, large-scale screening of alanine racemase inhibitors against A. hydrophila that could be applied in the development of new antibiotics against A. hydrophila.