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1. PEP: Proactive Checkpointing for Efficient Preemption on GPUs

   https://doi.org/10.1109/DAC.2018.8465929  

   Li, Chen; Yang, Jun; Zigerelli, Andrew; Guo, Yang (June 2018, 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC),)
2. JUNO sensitivity to ⁷ Be, pep, and CNO solar neutrinos

   https://doi.org/10.1088/1475-7516/2023/10/022  

   Abusleme, Angel; Adam, Thomas; Ahmad, Shakeel; Ahmed, Rizwan; Aiello, Sebastiano; Akram, Muhammad; Aleem, Abid; Alexandros, Tsagkarakis; An, Fengpeng; An, Qi; et al (October 2023, Journal of Cosmology and Astroparticle Physics)

   Abstract The Jiangmen Underground Neutrino Observatory (JUNO), the first multi-kton liquid scintillator detector, which is under construction in China, will have a unique potential to perform a real-time measurement of solar neutrinos well below the few MeV threshold typical of Water Cherenkov detectors. JUNO's large target mass and excellent energy resolution are prerequisites for reaching unprecedented levels of precision. In this paper, we provide estimation of the JUNO sensitivity to⁷Be,pep, and CNO solar neutrinos that can be obtained via a spectral analysis above the 0.45 MeV threshold. This study is performed assuming different scenarios of the liquid scintillator radiopurity, ranging from the most optimistic one corresponding to the radiopurity levels obtained by the Borexino experiment, up to the minimum requirements needed to perform the neutrino mass ordering determination with reactor antineutrinos — the main goal of JUNO. Our study shows that in most scenarios, JUNO will be able to improve the current best measurements on⁷Be,pep, and CNO solar neutrino fluxes. We also perform a study on the JUNO capability to detect periodical time variations in the solar neutrino flux, such as the day-night modulation induced by neutrino flavor regeneration in Earth, and the modulations induced by temperature changes driven by helioseismic waves. 

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3. Dynamic regulation of Pep-induced immunity through post-translational control of defence transcript splicing

   https://doi.org/10.1038/s41477-020-0724-1  

   Dressano, Keini; Weckwerth, Philipp R.; Poretsky, Elly; Takahashi, Yohei; Villarreal, Carleen; Shen, Zhouxin; Schroeder, Julian I.; Briggs, Steven P.; Huffaker, Alisa (August 2020, Nature Plants)

   null (Ed.)
4. Pep-TCRNet: Prediction of Multi-Class Peptides by T-cell Receptor Sequences with Deep Learning

   https://doi.org/10.6084/m9.figshare.27874143.v2  

   Le, Phi; Ung, Leah; Yang, Hai; Huang, Anwen; He, Tao; Bruno, Peter; Oh, David; Keenan, Bridget; Zhang, Li (July 2025, Oxford University Press)

   Pep-TCRNet is a novel approach to constructing a prediction model that can evaluate the probability of recognition between a TCR and a peptide amino acid sequence while combining inputs such as TCR sequences, HLA types, and VJ genes.Pep-TCRNet operates in two key steps:Feature Engineering: This step processes different types of variables:TCR and peptide amino acid sequencing data: The model incorporates neural network architectures inspired by language representation models and graph representation model to learn the meaningful embeddings.Categorical data: Specialized encoding techniques are used to ensure optimal feature representation for HLA types and VJ genes.Prediction Model: The second step involves training a prediction model to evaluate the likelihood of a TCR recognizing a specific peptide, based on the features generated in the first step. 

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