Jun Li

1 week ago | chemistry-europe.onlinelibrary.wiley.com | Xiaonan Wang |Jun Li |Haoyu Yan |Sen Yao

Conflict of Interests The authors declare no conflict of interest. Supporting Information Filename Description cctc70019-sup-0001-SuppMat.docx875 KB Supporting Information References 1, , Chem. Soc. Rev. 2021, 50, 4359−4381. 2, , , , Catalysts 2021, 11, 1070. 3, , , , , ACS Catal. 2023, 13, 4719−4741. 4, , Scientia Sinica Chimica 2018, 48, 342−361. 5, , , , , Chem. Soc. Rev. 2021, 50, 4299−4358. 6, , , , , , , Chin. J. Chem. 2021, 39, 661−665. 7, , , , , ACS Catal.

1 month ago | nature.com | Junan Yan |Jun Li |Shanshan Liang |Wei Shao |He Zhu

AbstractUrination, a fundamental physiological process, is intricately regulated by central neural circuits. The periaqueductal gray (PAG) is a critical hub for urination control, yet the specific neuronal subtypes involved remain undefined. Here, we identified somatostatin (SST)-expressing neurons in the lateral and ventrolateral PAG (l/vlPAG) as key regulators of urination. SST+ neurons in the l/vlPAG exhibited activity closely aligned with urination onset in freely moving animals.

Jan 31, 2025 | advanced.onlinelibrary.wiley.com | Tong Shan |Jun Li |Qing Bai |Ming Qiu Zhang

Conflict of Interest The authors declare no conflict of interest. Supporting Information Filename Description adfm202419941-sup-0001-SuppMat.docx22.8 MB Supporting Information References 1, , , , , , , , , , Natl. Sci. Rev. 2024, 11, nwad311. 2, , , , , , , , , , Light: Sci. Appl. 2020, 9, 31. 3, , , , , , , , Sci. Adv. 2018, 4, aas9530. 4, , , , Nat. Rev. Mater. 2017, 2, 16100. 5, , , Mater. Today 2023, 62, 327. 6, , , , , , , , Chin. J. Polym. Sci. 2023, 41, 1629.

Jan 16, 2025 | mdpi.com | Jun Li |Arthur Ramirez |Hua-Chien Hsu |Narayanan Lakshminarasimhan

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Jan 14, 2025 | onlinelibrary.wiley.com | Bo Wang |Astronautics Nanjing |Jun Li |Chenting Zhao

Building damage assessment in the face of natural disasters is crucial for economic development, disaster relief, and post-disaster reconstruction. However, existing algorithms often overlook the impact of the disaster class when extracting difference features from high-resolution pre- and post-disaster image pairs obtained through satellite remote sensing, without considering the influence of the disaster type, that is, the different ways in which different disasters affect buildings.