NeuO for Neuronal Labeling in Zebrafish

10.18383/j.tom.2015.00127Tomography1130-3

CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection

The last decade has witnessed a surge in the interaction of people through social networking platforms. While there are several positive aspects of these social platforms, the proliferation has led them to become the breeding ground for cyber-bullying and hate speech. Recent advances in NLP have often been used to mitigate the spread of such hateful content. Since the task of hate speech detection is usually applicable in the context of social networks, we introduce CRUSH, a framework for hate speech detection using user-anchored self-supervision and contextual regularization. Our proposed approach secures ~ 1-12% improvement in test set metrics over best performing previous approaches on two types of tasks and multiple popular english social media datasets.Comment: Accepted in NAACL HLT 2022 (Long Paper

NeuO for Neuronal Labeling in Zebrafish

We report the application of our newly developed fluorescent probe 3-(benzylamino)-4,4-difluoro-5-(4-propyl-1H-1,2,3-triazol-1-yl)-8-(4-(2-hydroxyacetamido)phen-yl)-4-bora-3a,4a-diaza-s-indacene (NeuO) to label and image live neurons in zebrafish. Immersing zebrafish embryos in NeuO or injecting NeuO into zebrafish brain ventricles results in nontoxic in vivo neuronal labeling. We demonstrate the applicability of NeuO and envisage the potential of this compound as a rapid and simple labeling reagent for studying neuron development and degeneration