Acute cerebral blood flow and its connectivity in patients with anti-LGI1 autoimmune encephalitis: an arterial spin labeling study - Ping Jiang; Lei Wang; Dingfeng Sun; Ailiang Miao; Xiaoshan Wang (Brain Imaging and Behavior, 2026)

Selective expansion of TCF7-expressing tumor-reactive T cell subpopulations during ovarian tumor-infiltrating T cell production ex vivo - Dingfeng Liu; Ting Zhang; Qinli Sun; Dongli Cai; Yanan Lou; Genyu Wang; Bowen Xie; Yicheng Zhu; Chong Wang; Zhouping Lu; Chenfei Liu; Yuan Li; et al. (Science China Life Sciences, 2025)

A semantic segmentation network for red tide detection based on enhanced spectral information using HY-1C/D CZI satellite data - Kunpeng Sun; Guanghao Jiang; Ning Wang; Dingfeng Yu; Jing Teng; Song Gao; Juan Huang; Zheng Zhao; Yan Song; Lei Xin; Yi Ding (Marine Pollution Bulletin, 2025)

Modeling of wave-induced drift based on stepwise parameter calibration - Kui Zhu; Xueyao Chen; Lin Mu; Dingfeng Yu; Runze Yu; Zhaolong Sun; Tong Zhou (Frontiers in Marine Science, 2025)
The motion of waves in water causes the slow movement of drifting sea targets—a phenomenon usually ignored in target-drift prediction models for maritime search and rescue (SAR). This study examined the wave-induced drift’s influence on field-observation experiments involving two common, differently sized SAR targets—an offshore fishing vessel (OFV) and a person in the water (PIW)—using parameter stepwise calibration and machine-learning (ML) methods. The sample of wave-induced drift velocity was obtained by gradually separating current-induced (CI) drift’s and wind-induced (WI) drift’s influence from the target-drift velocity using the least-square method and AP98 model. A force analysis method and three ML methods, long short-term memory (LSTM), back-propagation (BP) neural network, and random forest (RF), were used to fit the wave-induced drift velocity by combining eight different parameter schemes. Finally, the drift trajectories considering the influence of waves were fitted and verified based on 2 independent samples respectively. Compared with the force analysis method, the accuracy of the ML methods in the verification test was higher. In addition, the results show that for OFVs, considering wave-induced drift’s influence in the ensemble-trajectory prediction could improve the simulation accuracy. However, for a PIW, no significant improvement was observed. This result also indicates that wave-induced drift may not be simply ignored in large SAR targets’ drift prediction.

Experimental and numerical studies on the seismic response of adjacent horizontal parallel tunnels - Guobo Wang; Longfei Shi; Jianning Wang; Pengfei Zuo; Dingfeng Zhao; Fuxue Sun (Soil Dynamics and Earthquake Engineering, 2023)

Biomarkers of aging - Hainan Bao; Jiani Cao; Mengting Chen; Min Chen; Wei Chen; Xiao Chen; Yanhao Chen; Yu Chen; Yutian Chen; Zhiyang Chen; Jagadish K. Chhetri; Yingjie Ding; et al. (Science China Life Sciences, 2023)

The landscape of aging - Yusheng Cai; Wei Song; Jiaming Li; Ying Jing; Chuqian Liang; Liyuan Zhang; Xia Zhang; Wenhui Zhang; Beibei Liu; Yongpan An; Jingyi Li; Baixue Tang; et al. (Science China Life Sciences, 2022)

A field study on the application of distributed temperature sensing technology in thermal response tests for borehole heat exchangers - Dingfeng Cao; Bin Shi; Hong-Hu Zhu; Guangqing Wei; Hainar Bektursen; Mengya Sun (Bulletin of Engineering Geology and the Environment, 2019)