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AIRE relies on Z-DNA to flag gene targets for thymic T cell tolerization

AIRE is an unconventional transcription factor that enhances the expression of thousands of genes in medullary thymic epithelial cells and promotes clonal deletion or phenotypic diversion of self-reactive T cells1–4. The biological logic of AIRE’s target specificity remains largely unclear as, in contrast to many transcription factors, it does not bind to a particular DNA sequence motif. Here we implemented two orthogonal approaches to investigate AIRE’s cis-regulatory mechanisms: construction of a convolutional neural network and leveraging natural genetic variation through analysis of F1 hybrid mice5. Both approaches nominated Z-DNA and NFE2–MAF as putative positive influences on AIRE’s target choices. Genome-wide mapping studies revealed that Z-DNA-forming and NFE2L2-binding motifs were positively associated with the inherent ability of a gene’s promoter to generate DNA double-stranded breaks, and promoters showing strong double-stranded break gen ....

Las Vegas , Association For Computational Linguistics , Cell Biol , Acids Res , Computational Linguistics , Computer Vision , Pattern Recognition , Genome Biol , Unique Molecular Identifiers ,

Thailand develops local AI GPT model

Thailand develops local AI GPT model
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Chai Wutiwiwatchai , Association For Computational Linguistics , Supercomputer Center , Artificial Intelligence Entrepreneur Association Of Thailand , National Electronics , Computer Technology Center , Artificial Intelligence Entrepreneur Association , Computational Linguistics ,

GitHub - microsoft/LLMLingua: To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.

To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss. - GitHub - microsoft/LLMLingua: To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss. ....

Llmlingua Longllmlingua , Yuqing Yang , Xufang Luo , Qianhui Wu , Lili Qiu , Huiqiang Jiang , Dongsheng Li , Association For Computational Linguistics , Compressing Prompts , Accelerated Inference , Large Language Models , Chin Yew Lin , Long Context Scenarios , Prompt Compression , Under Review , Large Language , Empirical Methods , Natural Language , Online Meeting , Contributor License Agreement , Microsoft Open Source Code ,