There is increasing evidence that inducing neuronal mitophagy can be used as a therapeutic intervention for Alzheimer’s disease. Here, we screen a library of 2024 FDA-approved drugs or drug candidates, revealing UMI-77 as an unexpected mitophagy activator. UMI-77 is an established BH3-mimetic for MCL-1 and was developed to induce apoptosis in cancer cells. We found that at sub-lethal doses, UMI-77 potently induces mitophagy, independent of apoptosis. Our mechanistic studies discovered that MCL-1 is a mitophagy receptor and directly binds to LC3A. Finally, we found that UMI-77 can induce mitophagy in vivo and that it effectively reverses molecular and behavioral phenotypes in the APP/PS1 mouse model of Alzheimer’s disease. Our findings shed light on the mechanisms of mitophagy, reveal that MCL-1 is a mitophagy receptor that can be targeted to induce mitophagy, and identify MCL-1 as a drug target for therapeutic intervention in Alzheimer’s disease.
An enormous amount of conversation occurs online every day, such as on chat platforms where multiple conversations may take place concurrently. Interleaved conversations lead to difficulties in not only following discussions but also retrieving relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations.In this paper, we propose to leverage representation learning for conversation disentanglement. A Siamese hierarchical convolutional neural network (SHCNN), which integrates local and more global representations of a message, is first presented to estimate the conversation-level similarity between closely posted messages. With the estimated similarity scores, our algorithm for conversation identification by similarity ranking (CISIR) then derives conversations based on highconfidence message pairs and pairwise redundancy. Experiments were conducted with four publicly available datasets of conversations from Reddit and IRC channels. The experimental results show that our approach significantly outperforms comparative baselines in both pairwise similarity estimation and conversation disentanglement.
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