Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Comprehensive analysis of molecular characteristics between primary and breast-derived metastatic ovarian cancer
    (AME Publishing Company, 2025-03-30) Long J; Liu B; Li J; Ji X; Zhu N; Zhuang X; Wang H; Li L; Chen Y; Li X; Zhao S
    Background: The molecular basis for the disparities between primary ovarian cancer (POC) and ovarian cancer secondary to breast cancer (OCSTBC) remains poorly understood. This study aimed to explore the different characteristics between them through genomic analysis. Methods: We performed differentially expressed genes (DEGs) analysis between POC (n=96) and OCSTBC (n=44) groups with transcriptome data and revealed the enriched biological pathways with Kyoto Encyclopedia of Genes and Genomes (KEGG) and Hallmark gene sets between these two groups. Then, the Microenvironment Cell Populations (MCP)-counter and Cell-type Identification by Estimating Relative Subsets of RNA Transcript (CIBERSORT) algorithms were applied to evaluate the immune infiltration in tumor microenvironment (TME) between them. Finally, we performed the association analysis within single nucleotide polymorphism (SNP) data and obtained some meaningful SNPs and candidate genes for further transcriptomic analysis. Results: We identified a total of 13 cancer-related genes including GATA3, FOXA1, CCND1, and TTK between POC (n=96) and OCSTBC (n=44) groups with DEGs analysis. Integrated analysis revealed more significant immune-enriched pathways in the POC than in the OCSTBC group. Most immune cells had higher infiltration abundance in POC, except M2 macrophages, which was higher in OCSTBC. In SNP analysis, four SNP regions (8q12.1, 11q21, 11q24.3, and 17q25.3) were found to be significantly correlated with phenotypes (POC/OCSTBC), and importantly, some new susceptibility genes such as ETS1, CWC15, and XKR4 were revealed to potentially be associated with distinction between POC and OCSTBC. Conclusions: Our study provides a systematic molecular characteristic between POC and OCSTBC and suggests a pressing need to develop some specific therapeutic strategies in certain types of ovarian cancer.
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    Transformer-based multiple instance learning network with 2D positional encoding for histopathology image classification
    (Springer Nature Switzerland AG, 2025-05) Yang B; Ding L; Li J; Li Y; Qu G; Wang J; Wang Q; Liu B
    Digital medical imaging, particularly pathology images, is essential for cancer diagnosis but faces challenges in direct model training due to its super-resolution nature. Although weakly supervised learning has reduced the need for manual annotations, many multiple instance learning (MIL) methods struggle to effectively capture crucial spatial relationships in histopathological images. Existing methods incorporating positional information often overlook nuanced spatial correlations or use positional encoding strategies that do not fully capture the unique spatial dynamics of pathology images. To address this issue, we propose a new framework named TMIL (Transformer-based Multiple Instance Learning Network with 2D positional encoding), which leverages multiple instance learning for weakly supervised classification of histopathological images. TMIL incorporates a 2D positional encoding module, based on the Transformer, to model positional information and explore correlations between instances. Furthermore, TMIL divides histopathological images into pseudo-bags and trains patch-level feature vectors with deep metric learning to enhance classification performance. Finally, the proposed approach is evaluated on a public colorectal adenoma dataset. The experimental results show that TMIL outperforms existing MIL methods, achieving an AUC of 97.28% and an ACC of 95.19%. These findings suggest that TMIL’s integration of deep metric learning and positional encoding offers a promising approach for improving the efficiency and accuracy of pathology image analysis in cancer diagnosis.
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    Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
    (MDPI (Basel, Switzerland), 2025-01-27) Wang H; Wei L; Liu B; Li J; Li J; Fang J; Mooney C; Gegov A; Jafari R; Arabikhan F
    Breast cancer is one of the most prevalent cancers among women, with early detection playing a critical role in improving survival rates. This study introduces a novel transformer-based explainable model for breast cancer lesion segmentation (TEBLS), aimed at enhancing the accuracy and interpretability of breast cancer lesion segmentation in medical imaging. TEBLS integrates a multi-scale information fusion approach with a hierarchical vision transformer, capturing both local and global features by leveraging the self-attention mechanism. This model addresses the limitations of existing segmentation methods, such as the inability to effectively capture long-range dependencies and fine-grained semantic information. Additionally, TEBLS incorporates visualization techniques to provide insights into the segmentation process, enhancing the model’s interpretability for clinical use. Experiments demonstrate that TEBLS outperforms traditional and existing deep learning-based methods in segmenting complex breast cancer lesions with variations in size, shape, and texture, achieving a mean DSC of 81.86% and a mean AUC of 97.72% on the CBIS-DDSM test set. Our model not only improves segmentation accuracy but also offers a more explainable framework, which has the potential to be used in clinical settings.
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    Evolutionary Optimization of Energy Consumption and Makespan of Workflow Execution in Clouds
    (MDPI (Basel, Switzerland), 2023-04-30) Xing L; Li J; Cai Z; Hou F; Sanz JA
    Making sound trade-offs between the energy consumption and the makespan of workflow execution in cloud platforms remains a significant but challenging issue. So far, some works balance workflows’ energy consumption and makespan by adopting multi-objective evolutionary algorithms, but they often regard this as a black-box problem, resulting in the low efficiency of the evolutionary search. To compensate for the shortcomings of existing works, this paper mathematically formulates the cloud workflow scheduling for an infrastructure-as-a-service (IaaS) platform as a multi-objective optimization problem. Then, this paper tailors a knowledge-driven energy- and makespan-aware workflow scheduling algorithm, namely EMWSA. Specifically, a critical task adjustment-based local search strategy is proposed to intelligently adjust some critical tasks to the same resource of their successor tasks, striving to simultaneously reduce workflows’ energy consumption and makespan. Further, an idle gap reuse strategy is proposed to search the optimal energy consumption of each non-critical task without affecting the operation of other tasks, so as to further reduce energy consumption. Finally, in the context of real-world workflows and cloud platforms, we carry out comparative experiments to verify the superiority of the proposed EMWSA by significantly outperforming 4 representative baselines on 19 out of 20 workflow instances.
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    Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling
    (Springer Nature, 2023-06-29) Li J; Xing L; Zhong W; Cai Z; Hou F
    Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.
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    Can citrus farmers earn more from selling online
    (Elsevier B.V on behalf of the Economic Society of Australia, Queensland, 2023-12) Zhang H; Ma W; Li J; Yang W
    Online sales are essential for linking smallholder farmers to a wide range of markets. In essence, online sales not only influence the income received from selling a specific product but also generate spillover effects on total farm income and household income because they promote the sales of other agricultural products and generate regional off-farm work opportunities (e.g. product sorting, packaging, and delivery). Taking citrus as an example, this study explores the income effects of online sales with a focus on net returns from citrus production, net farm income, and household income. We used an endogenous treatment regression model to address the self-selection bias issues of online sales and estimated data collected from 926 citrus-producing households in Jiangxi Province, China. The results show that online citrus sales boost income growth in rural China. Specifically, online sales significantly increased net returns from citrus production, net farm income, and household income by 5,000 Yuan/capita, 8,580 Yuan/capita, and 17,830 Yuan/capita, respectively. The income-enhancing effects of online sales are greater for female household heads than they are for their male counterparts. Our findings emphasise the importance of promoting online sales to improve rural household welfare.
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    A Two-State Dynamic Decomposition-Based Evolutionary Algorithm for Handling Many-Objective Optimization Problems
    (MDPI (Basel, Switzerland), 2023-01-17) Xing L; Li J; Cai Z; Hou F; Pan L; Cui Z; Garg H
    Decomposition-based many-objective evolutionary algorithms (D-MaOEAs) are brilliant at keeping population diversity for predefined reference vectors or points. However, studies indicate that the performance of an D-MaOEA strongly depends on the similarity between the shape of the reference vectors (points) and that of the PF (a set of Pareto-optimal solutions symbolizing balance among objectives of many-objective optimization problems) of the many-objective problem (MaOP). Generally, MaOPs with expected PFs are not realistic. Consequently, the inevitable weak similarity results in many inactive subspaces, creating huge difficulties for maintaining diversity. To address these issues, we propose a two-state method to judge the decomposition status according to the number of inactive reference vectors. Then, two novel reference vector adjustment strategies, set as parts of the environmental selection approach, are tailored for the two states to delete inactive reference vectors and add new active reference vectors, respectively, in order to ensure that the reference vectors are as close as possible to the PF of the optimization problem. Based on the above strategies and an efficient convergence performance indicator, an active reference vector-based two-state dynamic decomposition-base MaOEA, referred to as ART-DMaOEA, is developed in this paper. Extensive experiments were conducted on ART-DMaOEA and five state-of-the-art MaOEAs on MaF1-MaF9 and WFG1-WFG9, and the comparative results show that ART-DMaOEA has the most competitive overall performance.
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    Effect of iron-manganese oxide on the degradation of deoxynivalenol in feed and enhancement of growth performance and intestinal health in weaned piglets.
    (Elsevier B.V., 2024-10-28) Wu C; Song J; Liu X; Zhang Y; Zhou Z; Thomas DG; Wu B; Yan X; Li J; Zhang R; Wu F; Cheng C; Pu X; Wang X
    Deoxynivalenol (DON), a prevalent and highly toxic mycotoxin in animal feed, poses significant risks to livestock health and productivity. This study evaluates the effectiveness of iron-manganese oxide (Fe/Mn oxides) in degrading DON. The DON degradation rate of Fe/Mn oxide reached 98.46 % in a controlled solution under specific conditions (0.2 % concentration, 37-85 °C, pH 6-7, 1-minute reaction time). When applied to actual feed, it reduced DON levels by approximately 49.3 % and remained stable in simulated gastrointestinal environments of weaned piglets. A 28-day trial involving 48 weaned piglets assessed the impacts of Fe/Mn oxides on health and growth. Results indicated that piglets consuming contaminated feed without the treatment exhibited reduced growth and compromised gut integrity, which were significantly mitigated by the addition of Fe/Mn oxides. Therefore, Fe/Mn oxides effectively reduce DON in feed and alleviate adverse health effects in piglets, making them a viable option to enhance safety and performance in mycotoxin-prone environments.
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    The Complementarity of Amino Acids in Cooked Pulse/Cereal Blends and Effects on DIAAS
    (MDPI (Basel, Switzerland), 2021-09-24) Han F; Moughan PJ; Li J; Stroebinger N; Pang S; Spina A; Pasqualone A
    The aim was to study the complementary effect between cereals and pulses on protein quality. The values for the digestible indispensable amino acid score (DIAAS) in cooked cereals and pulses, given alone, and blends of cooked cereals and pulses, were determined. True ileal digestibility (TID) values of amino acids for adult humans were obtained. It is difficult to determine ileal amino acid digestibility in humans directly, and for this reason, the growing pig is often used to obtain such values, as a preferred animal model. Seven growing pigs fitted with a T-cannula at the terminal ileum were allotted to a 7 × 6 incomplete Latin square with seven semi-synthetic diets (cooked mung bean, adzuki bean, millet, adlay, mung bean + millet, adzuki bean + adlay, and an N-free diet) and six 7-day periods. The mean TID values for crude protein differed significantly (p < 0.05), with millet having the highest digestibility (89.4%) and the adzuki bean/adlay mixture having the lowest (79.5%). For lysine, adzuki bean had the highest TID (90%) and millet had the lowest (70%). For the mean of all the amino acids, there was a significant (p < 0.05) effect of diet, with the TID ranging from 72.4% for the adzuki bean/adlay mixture to 89.9% for the adzuki beans. For the older child, adolescent, and adult, the DIAAS (%) was 93 for mung beans, 78 for adzuki beans, 22 for millet, 16 for adlay, and 66 for mung beans + millet, and 51 for adzuki beans + adlay. For mung beans, valine was first-limiting, and the SAA for adzuki beans, while lysine was first-limiting for the other foods. Chinese traditional diets, containing both cereals and pulses, are complementary for most, but not all of the indispensable amino acids.
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    Integrative analysis identifies two molecular and clinical subsets in Luminal B breast cancer
    (Elsevier Inc, 2023-09-15) Wang H; Liu B; Long J; Yu J; Ji X; Li J; Zhu N; Zhuang X; Li L; Chen Y; Liu Z; Wang S; Zhao S
    Comprehensive multiplatform analysis of Luminal B breast cancer (LBBC) specimens identifies two molecularly distinct, clinically relevant subtypes: Cluster A associated with cell cycle and metabolic signaling and Cluster B with predominant epithelial mesenchymal transition (EMT) and immune response pathways. Whole-exome sequencing identified significantly mutated genes including TP53, PIK3CA, ERBB2, and GATA3 with recurrent somatic mutations. Alterations in DNA methylation or transcriptomic regulation in genes (FN1, ESR1, CCND1, and YAP1) result in tumor microenvironment reprogramming. Integrated analysis revealed enriched biological pathways and unexplored druggable targets (cancer-testis antigens, metabolic enzymes, kinases, and transcription regulators). A systematic comparison between mRNA and protein displayed emerging expression patterns of key therapeutic targets (CD274, YAP1, AKT1, and CDH1). A potential ceRNA network was developed with a significantly different prognosis between the two subtypes. This integrated analysis reveals a complex molecular landscape of LBBC and provides the utility of targets and signaling pathways for precision medicine.