Hydrolyzed konjac glucomannan retards egg white protein digestion via glycosylation -driven gastric texture and rheology modulation - Lulu Guo; Yicheng Guo; Yating Wu; Shende Hu; Xiaosan Wang; Luping Gu; Cuihua Chang; Junhua Li; Yanjun Yang; Yujie Su (Food Hydrocolloids, 2026)

pH-Shifting combined with heat treatment regulates the aggregate structure and interfacial functional properties of egg white protein microparticles powder - Yating Wu; Yicheng Guo; Junhua Li; Luping Gu; Yanjun Yang; Cuihua Chang; Yujie Su (Food Research International, 2026)

Correlation analysis of monosaccharide composition and in vitro antioxidant and hypoglycemic activities of Tremella fuciformis spore polysaccharides - Jianqiu Chen; Xiaoyu He; Shuting Zhou; Yujie Chen; Chunyuan Kong; Yating Deng; Shikai Ruan; Chengzhen Gu; Shujing Sun (Food Chemistry, 2026)

An integrated scenario-based approach for carbon reduction pathway decision making - Yating Zhao; Chang Tang; Alun Gu; Yuanfeng Wang; Yahong Dong (Sustainable Cities and Society, 2025)

A phosphodiesterase CpdB in Yersinia pseudotuberculosis degrades CDNs to inhibit innate immune response - Xiao Wang; Xinwei Hao; Yuqing Yang; Siyu Jia; Yating Chen; Wenguang Yang; Yi Luo; Zhen Xie; Yanchao Gu; Yuxuan Wu; Fuhua Zhang; Mengyuan Li; et al. (Veterinary Microbiology, 2024)

A new method for creating androgenetic diploid fish from tetraploid carp sperm and their genetic composition analysis - Haoran Gu; Qilong Liu; Yi Fan; Qiong Liu; Yating Zhu; Conghui Yang; Shi Wang; Shaojun Liu (Aquaculture, 2024)

Heavy metal pollution status, children health risk assessment and source apportionment in farmland soils in a typical polluted area, Northwest China - Shun Chen; Yating Gao; Chengkui Wang; Huilin Gu; Mingkun Sun; Yuhui Dang; Shiwei Ai (Stochastic Environmental Research and Risk Assessment, 2024)

An endophytic fungus interacts with the defensin-like protein OsCAL1 to regulate cadmium allocation in rice - Tianyu Gu; Ziai Qi; Yating Wang; Siying Chen; Jing Yan; Huapeng Qiu; Yanxuan Yu; Zijun Fang; Junmin Wang; Jiming Gong (Molecular Plant, 2024)

Clinical and molecular characteristics of Klebsiella pneumoniae infection in a tertiary general hospital of Wuhan, China - Yating Xiang; Hongpan Tian; Qingsong Chen; Jihong Gu; Hongmao Liu; Cuixiang Wang; Yirong Li (European Journal of Clinical Microbiology & Infectious Diseases, 2024)
Objectives The aim of this study was to investigate the clinical and molecular characteristics of Klebsiella pneumoniae infection from a tertiary general hospital in Wuhan, China. Methods From December 2019 to August 2022, 311 non-duplicate isolates of K. pneumoniae were collected from a tertiary hospital in Wuhan. These comprised 140 carbapenem-resistant K. pneumoniae (CRKP) isolates and 171 carbapenem-susceptible K. pneumoniae (CSKP) isolates. The clinical characteristics of patients with K. pneumoniae infection were retrospectively collected. Polymerase chain reaction (PCR) assays were used to identify the main carbapenem resistance genes, virulence genes and multi-locus sequence typing (MLST) profiles of the isolates, and the Galleria mellonella infection model was used to determine their virulence phenotypes. Results Independent risk factors for CRKP infection were hypertension, neurological disorders, being admitted to the intensive care unit (ICU) and prior use of antibiotics. Patient with CRKP infection had higher mortality than those with CSKP infection (23.6% vs 14.0%, P < 0.05). One hundred and two sequence types (STs) were identified among the K. pneumoniae isolates, and the most prevalent ST type was ST11 (112/311, 36.0%). All of the ST11 isolates were CRKP. Among the 112 ST11 isolates, 105 (93.8%) harboured the carbapenem resistance gene bla KPC-2 (ST11- KPC-2 ), and of these isolates, 78 (74.3%, 78/105) contained all of the four virulence genes, namely rmpA, rmpA2 , iroN and iucA , suggesting that these genes were widespread among the isolates responsible for K. pneumoniae infections. Conclusion In this study, ST11- KPC-2 was responsible for most of the K. pneumoniae infection cases. Carbapenem resistance rather than the co-occurrence of the virulence genes rmpA , rmpA2 , iroN and iucA was associated with K. pneumoniae infection-related mortality during hospitalisation. Furthermore, a high proportion of ST11- KPC-2 isolates carried all of the four virulence genes.

Litter leachates transform soil bacterial composition enhancing nitrogen fixation in alpine meadow - Yang You; Zhouwen Ma; Yongchao Gu; Jingfei Ren; Yingxin Wang; Yating Li; Muhammad Kamran; Qingping Zhou; Fujiang Hou (Applied Soil Ecology, 2023)

The underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems in the Northern and Eastern United States - Yating Gu; Yingyi Zhao; Zhengfei Guo; Lin Meng; Kun Zhang; Jing Wang; Calvin K.F. Lee; Jing Xie; Yantian Wang; Zhengbing Yan; He Zhang; Jin Wu (Remote Sensing of Environment, 2023)

A Data-Driven Approach Based on Multivariate Copulas for Quantitative Risk Assessment of Concrete Dam - Chenfei Shao; Chongshi Gu; Zhenzhu Meng; Yating Hu (Journal of Marine Science and Engineering, 2019)
Risk assessment of dam’s running status is an important part of dam management. A data-driven method based on monitored displacement data has been applied in risk assessment, owing to its easy operation and real-time analysis. However, previous data-driven methods considered displacement data series at each monitoring point as an independent variable and assessed the running status of each monitoring point separately, without considering the correlation between displacement of different monitoring points. In addition, previous studies assessed the dam’s running status qualitatively, without quantifying the risk probability. To solve the above two issues, a displacement-data driven method based on a multivariate copula function is proposed in this paper. Multivariate copula functions can construct a joint distribution which reveals the relevance structure of random variables. We assumed that the risk probability of each dam section is independent and took monitoring points at one dam section as examples. Starting from the risk assessment of single monitoring points, we calculated the residual between the monitored displacement data and the modelled data estimated by the statistical model, and built a risk ratio function based on the residual. Then, using the multivariate copula function, we obtained a combined risk ratio of multi-monitoring points which took the correlation between each monitoring point into account. Finally, a case study was provided. The proposed method not only quantitatively assessed the probability of the real-time dam risk but also considered the correlation between the displacement data of different monitoring points.