Arbuscular mycorrhizal symbiosis enhances microbial contribution to mineral-associated organic carbon persistence in soil: Insights from soil microbial community and microbial necromass carbon - Yin LIU; Jin QIAN; Yueming ZHU; Jing HU; Bianhe LU; Yuxuan HE; Junwei SHEN (Pedosphere, 2026)

Does algal trophic mode affect the removal of norfloxacin? Insights from Tetradesmus obliquus - Musheng Ji; Jin Qian; Xiaxia Li; Yueming Zhu; Yin Liu; Bianhe Lu; Sijing Tang; Junwei Shen; Yuhang Zong (Journal of Hazardous Materials, 2026)

The combined exposure to antibiotics intensified the phytotoxicity of perovskite composite nanoparticles to Phragmites communis - Yuxuan He; Yueming Zhu; Jin Qian; Bianhe Lu; Sijing Tang; Yin Liu; Junwei Shen; Musheng Ji (Journal of Environmental Management, 2026)

Polystyrene nanoplastics reshape the peatland plants (Sphagnum) bacteriome under simulated wet-deposition pathway: Insights into unequal impact of ecological niches - Sijing Tang; Jin Qian; Yueming Zhu; Bianhe Lu; Yuxuan He; Yin Liu; Kailin Xu; Junwei Shen (Journal of Hazardous Materials, 2025)

Synergistic impact of plant essential oil based microemulsion on lychee preservation in comparison to Prochloraz - Yujie Shen; Xin Zhou; Jupeng Zhao; Zhe Wang; Dandan Zhang; Lingrong Wen; Yueming Jiang; Liang Gong (Sustainable Chemistry and Pharmacy, 2025)

MAPK signaling mediated intestinal inflammation induced by endoplasmic reticulum stress and NOD2 - Siyuan Peng; Yan Zhao; Wang Jiang; Yan Long; Tian Hu; Mengling Li; Jinyue Hu; Yueming Shen (Molecular and Cellular Biochemistry, 2025)

Interaction between root exudates and PFOS mobility: Effects on rhizosphere microbial health in wetland ecosystems - Bianhe Lu; Peifang Wang; Yueming Zhu; Jing Hu; Jin Qian; Yuanyuan Huang; Junwei Shen; Sijing Tang; Yin Liu (Environmental Pollution, 2025)

Fabrication of antimicrobial packaging based on polyaminopropyl biguanide incorporated pectin/polyvinyl alcohol films for fruit preservation - Mingyang He; Jinpeng Pan; Min Hong; Yujie Shen; Heng Zhang; Yueming Jiang; Liang Gong (Food Chemistry, 2024)

Ubiquitin ligase enzymes and de-ubiquitinating enzymes regulate innate immunity in the TLR, NLR, RLR, and cGAS-STING pathways - Wang Jiang; Mengling Li; Siyuan Peng; Tian Hu; Yan Long; Jiayi Zhang; Dan Peng; Yueming Shen (Immunologic Research, 2023)

Revealing the crystal phase effect of CeO2 in spinel-based nanocatalysts toward the peroxymonosulfate activation for organics degradation - Yuxuan He; Yueming Zhu; Jin Qian; Peifang Wang; Yuhang Zhang; Kailin Xu; Bianhe Lu; Yin Liu; Junwei Shen (Chemical Engineering Journal, 2023)

The impact of co-adopting electric vehicles, solar photovoltaics, and battery storage on electricity consumption patterns: Empirical evidence from Arizona - Xingchi Shen; Yueming Lucy Qiu; Xing Bo; Anand Patwardhan; Nathan Hultman; Bing Dong (Resources, Conservation and Recycling, 2023)

High-ratio {100} plane-exposed ZnO nanosheets with dual-active centers for simultaneous photocatalytic Cr(VI) reduction and Cr(III) adsorption from water - Xiao Li; Jiajia Liu; Jing Feng; Tong Wei; Zhongxiang Zhou; Jun Ma; Yueming Ren; Yanqing Shen (Journal of Hazardous Materials, 2023)

Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicine - Zonglin Liu; Yueming Wang; Fu Shen; Zhiyuan Zhang; Jing Gong; Caixia Fu; Changqing Shen; Rong Li; Guodong Jing; Sanjun Cai; Zhen Zhang; Yiqun Sun; et al. (EPMA Journal, 2022)
Background Currently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient. Aims This study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment. Methods A total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM. Findings The merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80–0.93] vs. 0.71 [95% CI: 0.59–0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76–0.90] vs. 0.68 [95% CI: 0.56–0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976–29.853], p < 0.001; HR = 6.427 [95% CI: 2.265–13.036], p = 0.002). Conclusion Our developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM.

The Impact of Multiple Pond Conditions on the Performance of Dike-Pond Extraction - Jinhao Zhou; Wu Zhou; Qiqi Zhou; Yuanhui Zhu; Fei Xie; Shen Liang; Yueming Hu (Fishes, 2022)
Dike-ponds in fisheries often present multiple pond conditions such as pure, suspended sediment, water bloom, semidry conditions, etc. However, the impact of these conditions on the performance of extracting dike-pond from remote sensing images has not been studied. To solve this problem, we explore the existence of such impacts by comparing the performance of four rule-based methods in two groups of test regions. The first group has few multiple pond conditions, while the second has more. The results show that various measure values deteriorate as the proportion of multiple pond conditions in the regions increases. All four methods performed worse in the second group than the first, where the overall accuracy decreased by 8.80%, misclassification error increased by 3.69%, omission error raised by 10.53%, and correct quantity rate dropped by 8.23%, respectively. The extraction method that ingested multiple pond conditions performed indistinguishably from the other methods in the first group. However, it outperformed the other methods in the second group, with a 4.22% improvement in overall accuracy, a 10.25% decrease in misclassification error, and a 19.03% increase in the correct quantity rate. These findings suggest that multiple pond conditions can negatively impact the extraction performance and should be considered in dike-pond applications that require a precise pond size, number, and shape.

Molecular characterization and dynamic expression patterns of two types of γ-gliadin genes from Aegilops and Triticum species - Shunli Wang; Xixi Shen; Pei Ge; Jie Li; Saminathan Subburaj; Xiaohui Li; F. J. Zeller; S. L. K. Hsam; Yueming Yan (Theoretical and Applied Genetics, 2012)