Target cell lysis by most murine cytotoxic T lymphocytes appears to be mediated by a complement (C9)-like protein called perforin, contained in high-density cytoplasmic granules. These granules also contain high levels of serine esterase activity, which may also play a role in cytolysis. Analysis of 17 cloned human cytotoxic T lymphocytes revealed the presence of serine esterase that is very similar to its murine counterpart in substrate and inhibitor specificities, pH optimum, and molecular mass; dot blot hybridization with synthetic oligonucleotides corresponding to the active sites of two known murine CTL esterases suggests homology to the murine enzyme HF. However, serine esterase was present at only approximately 10% of the level found in murine CTLs, and was not secreted during CTL-target cell interaction; moreover, hemolytic activity could not be detected in any of the seven cell lines tested. The results suggest that the human CTLs examined here kill their target cells by a mechanism different from that used by most cloned murine CTLs.

This letter is a retrospective analysis of our team's research for the Defense Advanced Research Projects Agency Explainable Artificial Intelligence project. Our initial approach was to use salience maps, English sentences, and lists of feature names to explain the behavior of deep-learning-based discriminative systems, with particular focus on visual question answering systems. We found that presenting static explanations along with answers led to limited positive effects. By exploring various combinations of machine and human explanation production and consumption, we evolved a notion of explanation as an interactive process that takes place usually between humans and artificial intelligence systems but sometimes within the software system. We realized that by interacting via explanations people could task and adapt machine learning (ML) agents. We added affordances for editing explanations and modified the ML system to act in accordance with the edits to produce an interpretable interface to the agent. Through this interface, editing an explanation can adapt a system's performance to new, modified purposes. This deep tasking, wherein the agent knows its objective and the explanation for that objective, will be critical to enable higher levels of autonomy.explainable artificial intelligence (XAI), human/computer interaction (HCI), tasking and adapting agents, visual question answering (VQA)