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1. Synergistic effects of Smac mimetic APG-1387 with anti-PD-1 antibody are attributed to increased CD3 + NK1.1 + cell recruitment secondary to induction of cytokines from tumor cells

Author

Wentao Pan, Qiuyun Luo, Eric Liang, Mude Shi, Jian Sun, Huimin Shen, Zhenhai Lu, Lin Zhang, Xianglei Yan, Luping Yuan, Suna Zhou, Hanjie Yi, Yifan Zhai, Miao-zhen Qiu, and Dajun Yang

Subjects
Smac mimetic, APG-1387, Combination therapy, Immunotherapy, CD3 + NK1.1 + cells, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Cytology, QH573-671
Abstract

Abstract Background Immune checkpoint inhibitors are approved for the treatment of various tumors, but the response rate is not satisfactory in certain malignancies. Inhibitor of apoptosis proteins (IAP) ubiquitin-E3 ligase activity is involved in the regulation of immune responses. APG-1387 is a novel second mitochondria-derived activator of caspase (Smac) mimetic IAP inhibitor. The aim of this study was to explore the synergistic effect of APG-1387 when combined with anti-PD-1 antibody in a preclinical setting. Methods We utilized syngeneic mouse models of ovarian cancer (ID8), colon cancer (MC38), malignant melanoma (B16), and liver cancer (Hepa1-6) to assess the combination effect of APG-1387 and anti-PD-1 antibody, including immune-related factors, tumor growth, and survival. MSD V-PLEX validated assays were used to measure in vitro and in vivo cytokine release. Results In ID8 ovarian cancer and MC38 colon cancer models, APG-1387 and anti-PD1 antibody had synergistic antitumor effects. In the MC38 model, the combination of APG-1387 and anti-PD-1 antibody significantly inhibited tumor growth (P

Published
2024
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2. Apple Intelligence Foundation Language Models

Author

Gunter, Tom, Wang, Zirui, Wang, Chong, Pang, Ruoming, Narayanan, Andy, Zhang, Aonan, Zhang, Bowen, Chen, Chen, Chiu, Chung-Cheng, Qiu, David, Gopinath, Deepak, Yap, Dian Ang, Yin, Dong, Nan, Feng, Weers, Floris, Yin, Guoli, Huang, Haoshuo, Wang, Jianyu, Lu, Jiarui, Peebles, John, Ye, Ke, Lee, Mark, Du, Nan, Chen, Qibin, Keunebroek, Quentin, Wiseman, Sam, Evans, Syd, Lei, Tao, Rathod, Vivek, Kong, Xiang, Du, Xianzhi, Li, Yanghao, Wang, Yongqiang, Gao, Yuan, Ahmed, Zaid, Xu, Zhaoyang, Lu, Zhiyun, Rashid, Al, Jose, Albin Madappally, Doane, Alec, Bencomo, Alfredo, Vanderby, Allison, Hansen, Andrew, Jain, Ankur, Anupama, Anupama Mann, Kamal, Areeba, Wu, Bugu, Brum, Carolina, Maalouf, Charlie, Erdenebileg, Chinguun, Dulhanty, Chris, Moritz, Dominik, Kang, Doug, Jimenez, Eduardo, Ladd, Evan, Shi, Fangping, Bai, Felix, Chu, Frank, Hohman, Fred, Kotek, Hadas, Coleman, Hannah Gillis, Li, Jane, Bigham, Jeffrey, Cao, Jeffery, Lai, Jeff, Cheung, Jessica, Shan, Jiulong, Zhou, Joe, Li, John, Qin, Jun, Singh, Karanjeet, Vega, Karla, Zou, Kelvin, Heckman, Laura, Gardiner, Lauren, Bowler, Margit, Cordell, Maria, Cao, Meng, Hay, Nicole, Shahdadpuri, Nilesh, Godwin, Otto, Dighe, Pranay, Rachapudi, Pushyami, Tantawi, Ramsey, Frigg, Roman, Davarnia, Sam, Shah, Sanskruti, Guha, Saptarshi, Sirovica, Sasha, Ma, Shen, Ma, Shuang, Wang, Simon, Kim, Sulgi, Jayaram, Suma, Shankar, Vaishaal, Paidi, Varsha, Kumar, Vivek, Wang, Xin, Zheng, Xin, Cheng, Walker, Shrager, Yael, Ye, Yang, Tanaka, Yasu, Guo, Yihao, Meng, Yunsong, Luo, Zhao Tang, Ouyang, Zhi, Aygar, Alp, Wan, Alvin, Walkingshaw, Andrew, Lin, Antonie, Farooq, Arsalan, Ramerth, Brent, Reed, Colorado, Bartels, Chris, Chaney, Chris, Riazati, David, Yang, Eric Liang, Feldman, Erin, Hochstrasser, Gabriel, Seguin, Guillaume, Belousova, Irina, Pelemans, Joris, Yang, Karen, Vahid, Keivan Alizadeh, Cao, Liangliang, Najibi, Mahyar, Zuliani, Marco, Horton, Max, Cho, Minsik, Bhendawade, Nikhil, Dong, Patrick, Maj, Piotr, Agrawal, Pulkit, Shan, Qi, Fu, Qichen, Poston, Regan, Xu, Sam, Liu, Shuangning, Rao, Sushma, Heeramun, Tashweena, Merth, Thomas, Rayala, Uday, Cui, Victor, Sridhar, Vivek Rangarajan, Zhang, Wencong, Zhang, Wenqi, Wu, Wentao, Zhou, Xingyu, Liu, Xinwen, Zhao, Yang, Xia, Yin, Ren, Zhile, and Ren, Zhongzheng

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.

Published
2024

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