Contamination profiles of legacy and emerging per- and polyfluoroalkyl substances in various vegetable oils collected in Chinese markets - Wenfeng Feng; Yuepeng Lu; Yuning Hu; Jing Cheng; Ling Wang; Zhen Zhou; Yong Liang (Journal of Environmental Sciences, 2026)

Preparation of hollow spherical B-CQDs-Ce2S3-Bi2WO6 heterostructures and enhanced photocatalytic degradation of minocycline via multi-orbital hybridization (p-p/p-d) - Yuning Zhang; Jian Zhang; Bangshuai Wang; Xinyan Wang; Haina Bai; Xin Cheng (Journal of Water Process Engineering, 2026)

Photocatalytic-peroxymonosulfate dual activation strategy: Efficient removal of norfloxacin by Z-type heterojunction CoWO4/α-Fe2O3 composite catalyst - Jian Zhang; Xinyan Wang; Zixin Liu; Xin Cheng; Haina Bai; Yuning Zhang; Ruoyi Li (Journal of Environmental Chemical Engineering, 2026)

Utilisation of Machine Learning Approaches Improves RNA-Seq Transcriptome Analyses in Alzheimer’s Disease Brain - Yuning Cheng; Kristina Santucci; Yulan Gao; Konii Takenaka; Grace Lindner; Si-Mei Xu; Michael Janitz (Journal of Molecular Neuroscience, 2026)
Alzheimer’s disease (AD) is a neurodegenerative disorder that progressively deteriorates a person’s memory, as well as their ability to think and move. It has been reported to be the most common cause of dementia. Alterations in gene expression have been increasingly recognised as key contributors to the onset and progression of AD, driving interest in transcriptomic approaches to better understand the disease at a molecular level. The development of machine learning (ML) approaches in transcriptomics have been rapid in the past decade, and this advancement can be applied to the study of AD transcriptomes. An ML program that enhances the alignment data through filtering out low confidence splice junction reads, Splam, has been developed by Chao et al. (2023). However, this program has not been utilised and assessed in the transcriptomic study of a complex neurological disease such as AD. This study investigates both the transcriptome of AD brain and the potential of an ML program to enhance alignment-stage data quality and influence downstream analyses. Using the Integrative Genomics Viewer, a selection of filtered reads was visualised, uncovering the types of splice junction reads Splam discards to refine the alignment data. From the differential expression (DE) analysis, we found a higher number of DE transcripts using ML-filtered data compared to unfiltered data, potentially unmasking aspects of AD brain DE profile obscured by alignment noise. The gene loci expressing those transcripts were also determined to be more AD-relevant by comparing these findings with external studies, and contribute to more related gene ontology enrichment terms. We identified gene loci expressing transcripts of interest shared between ML-filtered and unfiltered data, as this consistency in detection suggests that these genes are robust candidates for downstream analyses and biomarkers in AD.

Drought-induced shifts in rhizosphere microbiota correlate with reprogramming of floral volatiles in Chrysanthemum morifolium ‘Gongju’ - Zhuannan Chu; Mingzhe Cheng; Yuning Sun; Weiwen Li; Dongmei Yin (Rhizosphere, 2025)

GHydroMod-SIM-DAI v1.0: A new distributed hydrological model capable of dynamically recognizing runoff generation mechanisms and accounting for aboveground and underground anthropogenic impacts - Yuhao Wang; Ke Zhang; Yuning Luo; Qinuo Zhang; Zhijia Li; Cheng Yao; Xinyu Chen; Sheng Wang; Jie Liu; Chuntang Zhang; Shunzhang Li (Journal of Hydrology, 2025)

The influence of γ-aminobutyric acid production and fat content on the gel properties, protein conformation, and nutritional characteristics of fermented milk: Achieved through interactions with milk proteins - Kangyong Zhang; Yang Cheng; Yuning Yang; Yuxing Li; Bailiang Li; Fei Liu (Food Chemistry, 2025)

Discovery of Novel Protein-Coding and Long Non-coding Transcripts in Distinct Regions of the Human Brain - Kristina Santucci; Yuning Cheng; Si-Mei Xu; Yulan Gao; Grace Lindner; Konii Takenaka; Michael Janitz (Journal of Molecular Neuroscience, 2025)
Recent improvements in the accuracy of long-read sequencing (LRS) technologies have expanded the scope for novel transcriptional isoform discovery. Additionally, these advancements have improved the precision of transcript quantification, enabling a more accurate reconstruction of complex splicing patterns and transcriptomes. Thus, this project aims to take advantage of these analytical developments for the discovery and analysis of RNA isoforms in the human brain. A set of novel transcript isoforms was compiled using three bioinformatic tools, quantifying their expression across eight replicates of the cerebellar hemisphere, five replicates of the frontal cortex, and six replicates of the putamen. By taking a subset of the novel isoforms consistent across all discovery methods, a set of 170 highly confident novel RNA isoforms was curated for downstream analysis. This set consisted of 104 messenger RNAs (mRNAs) and 66 long non-coding RNAs (lncRNAs) isoforms. The detailed structure, expression, and potential encoded proteins of novel mRNA isoform BambuTx321 have been further described as an exemplary representative. Additionally, the tissue-specific expression [mean counts per million (CPM) of 5.979] of novel lncRNA, BambuTx1299, in the cerebellar hemisphere was observed. Overall, this project has identified and annotated several novel RNA isoforms across diverse tissues of the human brain, providing insights into their expression patterns and investigating their potential functional roles. Thus, this project has contributed to a more comprehensive understanding of the brain’s transcriptomic landscape for applications in basic research.

Assessing thermal comfort for the elderly in historical districts and proposing adaptive urban design strategies: A case study in Zhenjiang, China - Yingnan Li; Yuhan Zhang; Yuedong Wang; Zipeng Song; Zhicheng Zhou; Lu Ding; Cen Chen; Xi Jin; Yuning Cheng (Landscape and Ecological Engineering, 2025)

Investigation of the Circular Transcriptome in Alzheimer’s Disease Brain - Yulan Gao; Si-Mei Xu; Yuning Cheng; Konii Takenaka; Grace Lindner; Michael Janitz (Journal of Molecular Neuroscience, 2024)
Circular RNAs (circRNAs) are a subclass of non-coding RNAs which have demonstrated potential as biomarkers for Alzheimer’s disease (AD). In this study, we conducted a comprehensive exploration of the circRNA transcriptome within AD brain tissues. Specifically, we assessed circRNA expression patterns in the dorsolateral prefrontal cortex collected from nine AD-afflicted individuals and eight healthy controls. Utilising two circRNA detection tools, CIRI2 and CIRCexplorer2, we detected thousands of circRNAs and performed a differential expression analysis. CircRNAs which exhibited statistically significantly differential expression were identified as AD-specific differentially expressed circRNAs. Notably, our investigation revealed 120 circRNAs with significant upregulation and 1325 circRNAs displaying significant downregulation in AD brains when compared to healthy brain tissue. Additionally, we explored the expression profiles of the linear RNA counterparts corresponding to differentially expressed circRNAs in AD-afflicted brains and discovered that the linear RNA counterparts exhibited no significant changes in the levels of expression. We used CRAFT tool to predict that circUBE4B had potential to target miRNA named as hsa-miR-325-5p, ultimately regulated CD44 gene. This study provides a comprehensive overview of differentially expressed circRNAs in the context of AD brains, underscoring their potential as molecular biomarkers for AD. These findings significantly enhance our comprehension of AD’s underlying pathophysiological mechanisms, offering promising avenues for future diagnostic and therapeutic developments.

Local climate regionalization of the Tibetan Plateau: A data-driven scale-dependent analysis - Yuning Feng; Shihong Du; Klaus Fraedrich; Xiuyuan Zhang; Mingyi Du; Weiming Cheng (Theoretical and Applied Climatology, 2024)

A distributed hydrological model for semi-humid watersheds with a thick unsaturated zone under strong anthropogenic impacts: A case study in Haihe River Basin - Xinyu Chen; Ke Zhang; Yuning Luo; Qinuo Zhang; Jiaqi Zhou; Yazhou Fan; Pengnian Huang; Cheng Yao; Lijun Chao; Honghun Bao (Journal of Hydrology, 2023)

Hydrochar coupled with iodide for efficient photodegradation of perfluorooctanoic acid and perfluorooctane sulfonic acid under ultraviolet light - Yuning Hu; Yuhang Zhan; Cuiyun Wei; Fengjie Chen; Jing Cheng; Yunxiang Shen; Zhen Zhou; Ling Wang; Yong Liang (Science of The Total Environment, 2023)

Stitching electron localized heptazine units with “carbon patches” to regulate exciton dissociation behavior of carbon nitride for photocatalytic elimination of petroleum hydrocarbons - Qi Guo; Yuning Wu; Linhong Xia; Xue-Fang Yu; Kaisheng Zhang; Yujie Du; Lixue Zhang; Hua Tang; Jianbo Cheng; Jianpeng Shang; Yanhua Peng; Zhuo Li; et al. (Chemical Engineering Journal, 2023)

Is environmental regulation effective in promoting the quantity and quality of green innovation? - Xia Pan; Wenyin Cheng; Yuning Gao; Tomas Balezentis; Zhiyang Shen (Environmental Science and Pollution Research, 2021)

Flood-resilient urban design based on the indigenous landscape in the city of Can Tho, Vietnam - Nguyen Van Long; Yuning Cheng; Tu Dam Ngoc Le (Urban Ecosystems, 2020)

Modeling re-oxygenation performance of fine-bubble–diffusing aeration system in aquaculture ponds - Xiangju Cheng; Yuning Xie; Dantong Zhu; Jun Xie (Aquaculture International, 2019)