A novel continuous-flow three-stage tandem system based on partial nitrification/Anammox granular sludge and partial denitrification-Anammox biofilm (PN/A-PD-A) for advanced nitrogen removal from mature landfill leachate - Zhizhan Luo; Wei Li; Naixin Zhang; Mengen Lei; Bohan Chen; Yong Li; Qian Liu; Min Zhang; Sihao Lv; Faliang Cheng; Ji Li (Bioresource Technology, 2026)

Infra-slow body-surface potentials show a group-level association with thyroid cancer - Bohan Deng; Ruili Zhang; Yuxuan Liu; Jinglao Lin; Shicong Gui; Xihao Wei; Yin Cheng; Li Zheng; Shaohua Hu; Pingping Lyu; Yubo Li; Huafen Wang (Journal of Zhejiang University-SCIENCE B, 2026)

Enhancing soil organic carbon fixation with modified bentonite composites - Bing Liu; Yuwen Shen; Deshui Tan; Bohan Cheng; Haitao Lin; Rui Xue; Zongxin Li; Qiang Yao; Shuying Jiao (Applied Clay Science, 2025)

EnsembleAge: enhancing epigenetic age assessment with a multi-clock framework - Amin Haghani; Ake T. Lu; Qi Yan; Juan Carlos Izpisua Belmonte; Pradeep Reddy; Victor Cheng; X. William Yang; Nan Wang; Khyobeni Mozhui; Kevin Murach; Alejandro Ocampo; Robert W. Williams; et al. (GeroScience, 2025)
Several widely used epigenetic clocks have been developed for mice and other species, but a persistent challenge remains: different mouse clocks often yield inconsistent results. To address this limitation in robustness, we present EnsembleAge, a suite of ensemble-based epigenetic clocks. Leveraging data from over 200 perturbation experiments across multiple tissues, EnsembleAge integrates predictions from multiple penalized models. Empirical evaluations demonstrate that EnsembleAge outperforms existing clocks in detecting both pro-aging and rejuvenating interventions. Furthermore, we introduce EnsembleAge HumanMouse, an extension that enables cross-species analyses, facilitating translational research between mouse models and human studies. Together, these advances underscore the potential of EnsembleAge as a robust tool for identifying and validating interventions that modulate biological aging.

GMNN mutations cause female infertility characterized by preimplantation embryo arrest through regulating DNA re-replication - Honghui Zhang; Wei Su; Xiaohong Jiang; Yang Wang; Bohan Yang; Xian Wan; Cheng Li; Shuai Zhao; Changlong Zhang; Shigang Zhao; Yuehong Bian; Mei Li; et al. (Science China Life Sciences, 2025)

Enhancing winter wheat plant nitrogen content prediction across different regions: Integration of UAV spectral data and transfer learning strategies - Zongpeng Li; Qian Cheng; Li Chen; Jie Yang; Weiguang Zhai; Bohan Mao; Yafeng Li; Xinguo Zhou; Zhen Chen (Computers and Electronics in Agriculture, 2025)

Mass-balance analysis of sulfamethoxazole during extracellular-polymeric-substance extraction from excess sludge - Ming Cheng; Yijia Qian; Bohan Zhao; Nan Zhang; Lihua Sun; Liang Zhao; Ranbin Liu; Xiaodi Hao; Chen Shi (Journal of Water Process Engineering, 2025)

Prediction Methods for Antimicrobial Resistance Trends in China - Zhengyang Wu; Ning Zhang; Bohan Zhang; Haiwei Wang; Jiaqi Yan; Xingyu Wan; Ming Cheng; Junming Bu; Yinan Du (Indian Journal of Microbiology, 2025)

Novel spectral indices and transfer learning model in estimat moisture status across winter wheat and summer maize - Zongpeng Li; Qian Cheng; Li Chen; Weiguang Zhai; Bo Zhang; Bohan Mao; Yafeng Li; Fun Ding; Xinguo Zhou; Zhen Chen (Computers and Electronics in Agriculture, 2025)

Multi-random ensemble on Partial Least Squares regression to predict wheat yield and its losses across water and nitrogen stress with hyperspectral remote sensing - Bohan Mao; Qian Cheng; Li Chen; Fuyi Duan; Xiaoxiao Sun; Yafeng Li; Zongpeng Li; Weiguang Zhai; Fan Ding; Hao Li; Zhen Chen (Computers and Electronics in Agriculture, 2024)

Advanced anaerobic digestion of household food waste pretreated by in situ-produced mixed enzymes via solid-state fermentation: Feasibility and application perspectives - Lianpei Zou; Zhuoying Qi; Hui Cheng; Bohan Yu; Yu-You Li; Jianyong Liu (Environmental Research, 2024)

Experimental studies on the effects of thermal damage in coal-derived mudstones caused by high temperatures - Yanjun Shen; Ziyi Li; Cheng Peng; Bohan Yang; Tianchi Ma (Acta Geodaetica et Geophysica, 2023)

Manual correction of genome annotation improved alternative splicing identification of Artemisia annua - Zhaoyu Liu; Yupeng Du; Zhihao Sun; Bohan Cheng; Zenghao Bi; Zhicheng Yao; Yuting Liang; Huiling Zhang; Run Yao; Shen Kang; Yuhua Shi; Huihua Wan; et al. (Planta, 2023)

Pathogenicity of duck circovirus and fowl adenovirus serotype 4 co-infection in Cherry Valley ducks - Mingyue Shen; Panpan Gao; Cheng Wang; Ning Li; Shuyu Zhang; Yunxuan Jiang; Defeng Liu; Baoyu Jia; Li Xu; Bohan Huang; Ruiliang Zhu; Kai Wei (Veterinary Microbiology, 2023)

Photoactivatable metal organic framework for synergistic ferroptosis and photodynamic therapy using 450 nm laser - Yuhang Chen; Lijiang Gu; Bohan Ma; Xing Li; Yibo Mei; Jinlai Zhou; Yue Chong; Minghai Ma; Mengzhao Zhang; Lu Wang; Yifan Cheng; Kaijie Wu; et al. (Chemical Engineering Journal, 2023)

A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments - Liqian Wang; Shuzhen Fan; Yunxia Liu; Yongfu Li; Cheng Fei; Junliang Liu; Bohan Liu; Yakui Dong; Zhaojun Liu; Xian Zhao (Journal of Marine Science and Engineering, 2021)
The ocean connects all continents and is an important space for human activities. Ship detection with electro-optical images has shown great potential due to the abundant imaging spectrum and, hence, strongly supports human activities in the ocean. A suitable imaging spectrum can obtain effective images in complex marine environments, which is the premise of ship detection. This paper provides an overview of ship detection methods with electro-optical images in marine environments. Ship detection methods with sea–sky backgrounds include traditional and deep learning methods. Traditional ship detection methods comprise the following steps: preprocessing, sea–sky line (SSL) detection, region of interest (ROI) extraction, and identification. The use of deep learning is promising in ship detection; however, it requires a large amount of labeled data to build a robust model, and its targeted optimization for ship detection in marine environments is not sufficient.