Other Than Him: Superman as the Alien That Made Good

When discussing depictions of the alien in American popular culture—as extraterrestrial, as strange foreigner, and as both, an otherworldly Other—the most famous example is rarely considered; SUPERMAN. Introduced in 1938, this strange visitor from another planet possesses powers and abilities far beyond those of mortal men but walks among us disguised as a mild-mannered human. He’s the fantastic hiding in plain sight as the most mundane, an Other beloved as familiar, a singular being of another race who’s come to symbolize the best in humanity. And not by accident. Superman was created by two Cleveland teens, Jerry Siegel and Joe Shuster. They were Jews in the Midwest during the rise of Nazism abroad and at home, bashful geeks bullied by other boys and rejected by girls, one the son of immigrants from Eastern Europe and the other an immigrant himself. They were Others in every sense, and into their alien hero they poured all their wishes, to belong and be accepted but also to be exceptional and revered. To be special but also to blend in. To be both Super and man. They gave their assimilation/assertion fantasy a nebbish secret identity based on themselves and empowered him with their Jewish heritage; the origin story of Moses as a baby sent adrift to safety, the physical and moral strength of Samson the mighty lawman, and the mission of the Golem as an inhuman protector of his creators. They made him a refugee fleeing catastrophe on the eve of World War II and sent him to tear Nazi tanks apart nearly two years before the US joined the war. In following decades, Superman’s mostly Jewish writers, artists, and editors continued to borrow Judaic motifs for their stories, further exploring the character’s unique standing as an alien who’s accepted as human, an Other who’s come to embody our idealized selves. In the postwar era he was blamed for causing juvenile delinquency, in the sixties and seventies he underwent frequent Kafkaesque metamorphoses, in the eighties he unsuccessfully attempted to renounce his alien heritage and with it his Otherness, and more recently he’s been featured in various alternate narratives in which he turns evil, like The Dark Side comic books, Injustice video games and Zack Snyder’s Justice League film. Tech Mod: Tim Lenz

A Drosophila Model for Clostridium difficile Toxin CDT Reveals Interactions with Multiple Effector Pathways.

Clostridium difficile infections (CDIs) cause severe and occasionally life-threatening diarrhea. Hyper-virulent strains produce CDT, a toxin that ADP-ribosylates actin monomers and inhibits actin polymerization. We created transgenic Drosophila lines expressing the catalytic subunit CDTa to investigate its interaction with host signaling pathways in vivo. When expressed in the midgut, CDTa reduces body weight and fecal output and compromises survival, suggesting severe impairment of digestive functions. At the cellular level, CDTa induces F-actin network collapse, elimination of the intestinal brush border, and disruption of intercellular junctions. We confirm toxin-dependent re-distribution of Rab11 to enterocytes' apical surface and observe suppression of CDTa phenotypes by a Dominant-Negative form of Rab11 or RNAi of the dedicated Rab11GEF Crag (DENND4). We also report that Calmodulin (Cam) is required to mediate CDTa activity. In parallel, chemical inhibition of the Cam/Calcineurin pathway by Cyclosporin A or FK506 also reduces CDTa phenotypes, potentially opening new avenues for treating CDIs

Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases

NLP models often rely on superficial cues known as dataset biases to achieve impressive performance, and can fail on examples where these biases do not hold. Recent work sought to develop robust, unbiased models by filtering biased examples from training sets. In this work, we argue that such filtering can obscure the true capabilities of models to overcome biases, which might never be removed in full from the dataset. We suggest that in order to drive the development of models robust to subtle biases, dataset biases should be amplified in the training set. We introduce an evaluation framework defined by a bias-amplified training set and an anti-biased test set, both automatically extracted from existing datasets. Experiments across three notions of bias, four datasets and two models show that our framework is substantially more challenging for models than the original data splits, and even more challenging than hand-crafted challenge sets. Our evaluation framework can use any existing dataset, even those considered obsolete, to test model robustness. We hope our work will guide the development of robust models that do not rely on superficial biases and correlations. To this end, we publicly release our code and data.Comment: Findings of ACL 202