According to hordes of furious furry Twitter accounts, Tony the Tiger is on the purge. Back in the wild west days of file sharing, and for a moment all too brief, Kazaa reigned king. So for the sake of nostalgia, we ask you: What are some of your best which is to say, worst Kazaa memories? While most public figures have a hard time tweeting without at least one teen asking them to please sit on my face, daddy , the official Twitter account for Tony the Tiger , it seems, is dealing with an special breed of proposition. Because almost any time Tony tweets, the fawning furries of Twitter lose their shit. Sometime shortly after that, stock of the film mysteriously vanished from the site and everywhere else on the internet.

This is becoming common. I imagine the next step will be charging a child with child molestation for masturbating. This is what happens when you let puritans run the legal system.
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Chromatin interaction data from protocols such as ChIA-PET, HiChIP, and HiC provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. Gifford bioRxiv. The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Here we present a machine learning method that can design human Immunoglobulin G IgG antibodies with target affinities that are superior to candidates from phage display panning experiments within a limited design budget. We also demonstrate that machine learning can improve target-specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data. Significance Antibody based therapeutics must meet both affinity and specificity metrics, and existing in vitro methods for meeting these metrics are based upon randomization and empirical testing.