Discovery of cold tolerance genes and favorable alleles in Kam sweet rice across various growth stages - Kunchi Yu; Chunhui Liu; Joohyun Lee; Xiaoding Ma; Bing Han; Zhengwu Zhao; Soon-Wook Kwon; Longzhi Han; Di Cui (Theoretical and Applied Genetics, 2026)

Study on Long-Term Deformation Prediction and Creep-Damage Model of Rock - Yipin Liu; Tingting Ma; Shuguang Zhang; Xiaoding Wang; Xiaodong Sun; Wenbo Liu (Pure and Applied Geophysics, 2026)

Ectomycorrhizal and saprotrophic fungi are linked to reduced pathogenic fungi across roots and rhizosphere soils of major afforestation species in northeastern China - Wen Guo; Xiangxia Yang; Yaping Liu; Xiaoding Lin; Le Chang; Yaru Wei; Mingming Xia; Minghao Lv; Kazuo Isobe (Soil Ecology Letters, 2026)

Green Revolution-associated DELLA accumulation enhances salt tolerance in cereals by disrupting INDETERMINATE SPIKELET1 biomolecular condensates - Xiliu Cheng; Haoran Wang; Mingjie Lyu; Meiting Gao; Guogen Zhang; Yanjia Gong; Yanqing Chen; Xianjun Sun; Xiaoding Ma; Weihua Qiao; Fan Zhang; Jun Liu; et al. (Molecular Plant, 2026)

Advances in controllable targeted protein degradation: emerging strategies and mechanisms - Xiaoding Ma; Jianli Yin; Fan Ding; Guo Han; Xingwan Liu; Haifeng Ye (Science China Life Sciences, 2026)

Intelligent Diagnosis of Ship Propulsion Motor Bearings Based on Dynamic Class Weights - Guohua Yan; Xiaoding Wang; Kai Liu; Jingran Kang; Xinhua Yi (Journal of Marine Science and Engineering, 2025)
As an important part of the ship’s power system, the bearing operation status of the propulsion motor is directly related to the reliability and safety of the whole system. However, in the field of marine propulsion motor bearing fault diagnosis, the data imbalance problem seriously affects the performance of the fault detection model. Due to the scarcity of fault data relative to normal operation data, traditional diagnostic methods are ineffective in dealing with unbalanced data. To solve this problem, a dynamic class weighting solution is proposed. The dynamic class weighting method introduces the weight coefficient λ on the basis of the traditional class weighting, which can adjust the class weight value in real time according to the training situation, and comprehensively considers the data distribution and the training situation to ensure that the model can learn better even in the case of insufficient data. Testing on the imbalanced distribution of bearing natural-failure data shows that the proposed method achieves a 5.25% improvement in diagnostic accuracy compared to direct training. Compared with traditional class-weighted approaches, diagnostic accuracy is enhanced by 3.56%, effectively mitigating the impact of scarce and unevenly distributed failure data on model training.

Inhibition of recurrence and metastasis in triple-negative breast cancer through nanoparticle-mediated silencing of LPCAT1 to remodel ATP energy metabolism - Xiuling Li; Senlin Li; Haotian Li; Guo Wu; Huijie Zhao; Shaomin Liu; Xiaoding Xu; Phei Er Saw (Science China Life Sciences, 2025)

Multi-objective optimization of an autonomous underwater vehicle shape based on an improved Kriging model - Feng Liu; Xiaoding Deng (Ocean Engineering, 2024)

The resistance of hydrogenotrophic methanogenic microorganisms to ofloxacin in sludge anaerobic digestion process - Yanxin Wu; Chao Wu; Xuran Liu; Hongbo Chen; Zhiyi Deng; Dongbo Wang; Zhixing Wu; Shenglan Tang; Xiaoding Huang (Journal of Environmental Management, 2024)

Performance and mechanisms of citric acid improving biotransformation of waste activated sludge into short-chain fatty acids - Xiaodong Zhang; Xiaoding Huang; Xiaoming Li; Jiamin Zhang; Mengge Tang; Xuran Liu; Dongbo Wang; Qi Yang; Abing Duan; Junwu Liu (Bioresource Technology, 2023)

Genomic footprints of Kam Sweet Rice domestication indicate possible migration routes of the Dong people in China and provide resources for future rice breeding - Chunhui Liu; Tianyi Wang; Huicha Chen; Xiaoding Ma; Chengzhi Jiao; Di Cui; Bing Han; Xiaobing Li; Aixia Jiao; Renchao Ruan; Dayuan Xue; Yanjie Wang; et al. (Molecular Plant, 2023)

Corrigendum to “Novel diketopyrrolopyrrole NIR-II fluorophores and DDR inhibitors for in vivo Chemo-photodynamic therapy of Osteosarcoma” [Chem. Eng. J. 446 (2022) 136929] - Xiaoding Cheng; Chong Zhang; Kun Shen; Huifan Liu; Caihong Bai; Qihang Ding; Mengting Guan; Junzhu Wu; Zhiquan Tian; Deliang Chen; Lin Cai; Xuechuan Hong; et al. (Chemical Engineering Journal, 2023)

Damage Analysis of Soaking Gypsum and Safety Evaluation of Goaf: Based on Energy Dissipation Theory - Xiaoding Xu; Xuebin Cui; Xue Liu; Qiongqiong Tang; Xiaohu Zhang; Yanhua Sun (Geotechnical and Geological Engineering, 2020)

DNA Mismatch Repair Deficiency Detection in Colorectal Cancer by a New Microsatellite Instability Analysis System - Shafei Wu; Xiaoding Liu; Jing Wang; Weixun Zhou; Mei Guan; Yuanyuan Liu; Junyi Pang; Tao Lu; Liangrui Zhou; Xiaohua Shi; Huanwen Wu; Zhiyong Liang; et al. (Interdisciplinary Sciences: Computational Life Sciences, 2020)
Background Although microsatellite instability (MSI) is most commonly detected in colorectal cancer (CRC), improvement in MSI analysis method can always help us better assessing MSI phenotypes and gaining useful information in challenging cases. The purpose of current study is to explore whether the ProDx® MSI analysis System (ProDx® MSI) can improve MSI classification in CRC. Methods We compared the MSI profiles of 97 FFPE samples from CRC patients by ProDx® MSI with Promega MSI analysis System 1.2 and NCI panel. The result is then confirmed by IHC test, which evaluate MMR protein expression. Furthermore, next generation sequencing was performed to double confirm the specimens with discordant results. Results Among the total 97 CRC cases, 35 were scored as MSI-High by ProDx® MSI, Promega MSI analysis System 1.2, and NCI panel simultaneously. Three extra MSI-High cases were identified by ProDx® MSI. These three cases were classified as MSI-Low by NCI panel, while two of these as MSI-Low, and 1 as MSS by Promega MSI analysis System 1.2. ProDx® MSI had higher concordance with IHC detection compared with Promega MSI Analysis System 1.2 and NCI panel at 99.0%, 96.9%, and 95.9%, respectively. The ProDx® MSI distinguished MSI status with 100% sensitivity and 98.4% specificity. Our data showed that MSI-High phenotype occurred most frequently in tumor development stage I and stage II. Conclusions The colorectal cancer can be classified according to MSI status accurately by ProDx® MSI. More cases with MSI-High feature may be revealed by ProDx® MSI than by previous test systems in colorectal cancer.

A knockdown mutation of YELLOW-GREEN LEAF2 blocks chlorophyll biosynthesis in rice - Hong Chen; Zhijun Cheng; Xiaoding Ma; Han Wu; Yanling Liu; Kunneng Zhou; Yilin Chen; Weiwei Ma; Jingcui Bi; Xin Zhang; Xiuping Guo; Jiulin Wang; et al. (Plant Cell Reports, 2013)