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Like most urban transport models, the Brisbane Strategic Transport Model (BSTM) still applies conventional market segmentation structures for both students and commuters, where workers are categorised solely as ‘white collar’ and ‘blue collar’ workers, and students are categorised as ‘primary school’, ‘secondary school’ and ‘tertiary’ students. Such market segmentation structures are clearly insufficient to capture today’s students and commuters in transport models, which can cause significant adverse impact on the model performance. Australia currently has one of the highest rates of private school enrolment in the OECD. The post-industrial employment sector has been experiencing a significant rise of women’s participation, especially in the service sector. To improve transport model’s prediction accuracy, market segmentation structure may require an update to more effectively describe the variation in travel demands of school students and workers. This research seeks to address this problem. The overarching research questions are: i) how can we better conceive and apply segmentation of students to better model travel demand? and, ii) how can we better conceive and apply segmentation of workers to better model travel demand? In terms of approach and methods, the research project adopts deductive approach (i.e., conventional travel behaviour analysis study and policy analysis study) for the research question 1, exploring the travel behaviour difference between public and private school students using the 2017-2019 South East Queensland Travel Survey dataset and adopts inductive approach (i.e., unsupervised clustering analysis study) for the research question 2 to investigate the underlying commuters’ market segmentation structure using the 2017-2020 South East Queensland Travel Survey (SEQTS) dataset. To answer the first research question, the first ever comparative study to look at private school travel at a regional scale is conducted and suggests there is clearly different travel mode preference and trip distance for each mode between public and private school students in the Southeast Queensland context. To answer the second research question, a novel market segmentation structure for commuters is proposed, revealing the hidden gender gap in the conventional blue/white collar market segmentation for workers and highlighting the importance of ‘pink collar’ market segment which mainly represents for female workers. The main contributions of this research project include 1) conceptual innovations in understandings of private school travel and female workers’ travel behaviours, 2) methodological innovations in applying the k-nearest neighbour algorithm to overcome the data limitation and applying a more robust clustering analysis - unsupervised Partition Around Medoid(PAM) clustering technique in revealing meaningful commuter market segments for transport models, 3) applied contributions as the results from this research provide novel market segmentation structures for both students and commuters for transport models to improve its forecasting accuracy.

To analyze the pharmacological mechanism of Epimedium in regulating heart failure (HF) based on the network pharmacology method, and to provide a reference for the clinical application of Epimedium in treating HF. Obtaining the main active ingredients and their targets of Epimedium through TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) database. Access to major HF targets through Genecards, OMIM, PharmGKB, Therapeutic Target Database, Drug Bank database. Protein interaction analysis using String platform and construction of PPI network. Subsequently, Cytoscape software was used to construct the "Epimedium active ingredient-heart failure target" network. Finally, the molecular docking is verified through the Systems Dock Web Site. The core active ingredients of Epimedium to regulate HF are quercetin, luteolin, kaempferol, etc. The core targets are JUN, MYC, TP53, HIF1A, ESR1, RELA, MAPK1, etc. Molecular docking validation showed better binding activity of the major targets of HF to the core components of Epimedium. The biological pathways that Epimedium regulates HF mainly act on lipid and atherosclerotic pathways, PI3K-Akt signaling pathway, and chemoattractant-receptor activation. And its molecular functions are mainly DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, and neurotransmitter receptor activity. This study reveals the multi-component, multi-target and multi-pathway mechanism of action of Epimedium in regulating mental failure, and provides a basis for the clinical development and utilization of Epimedium to intervene in HF.

Heart failure is a global health problem and the number of sufferers is increasing as the population grows and ages. Existing diagnostic techniques for heart failure have various limitations in the clinical setting and there is a need to develop a new diagnostic model to complement the existing diagnostic methods. In recent years, with the development and improvement of gene sequencing technology, more genes associated with heart failure have been identified. We screened for differentially expressed genes in heart failure using available gene expression data from the Gene Expression Omnibus database and identified 6 important genes by a random forest classifier (ASPN, MXRA5, LUM, GLUL, CNN1, and SERPINA3). And we have successfully constructed a new heart failure diagnostic model using an artificial neural network and validated its diagnostic efficacy in a public dataset. We calculated heart failure-related differentially expressed genes and obtained 24 candidate genes by random forest classification, and selected the top 6 genes as important genes for subsequent analysis. The prediction weights of the genes of interest were determined by the neural network model and the model scores were evaluated in 2 independent sample datasets (GSE16499 and GSE57338 datasets). Since the weights of RNA-seq predictions for constructing neural network models were theoretically more suitable for disease classification of RNA-seq data, the GSE57338 dataset had the best performance in the validation results. The diagnostic model derived from our study can be of clinical value in determining the likelihood of HF occurring through cardiac biopsy. In the meantime, we need to further investigate the accuracy of the diagnostic model based on the results of our study.

Human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are unique cell sources for disease modeling, drug discovery screens, and cell therapy applications. The first step in producing neural lineages from hPSCs is the generation of neural stem cells (NSCs). Current methods of NSC derivation involve the time-consuming, labor-intensive steps of an embryoid body generation or coculture with stromal cell lines that result in low-efficiency derivation of NSCs. In this study, we report a highly efficient serum-free pluripotent stem cell neural induction medium that can induce hPSCs into primitive NSCs (pNSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. The pNSCs expressed the neural stem cell markers Pax6, Sox1, Sox2, and Nestin; were negative for Oct4; could be expanded for multiple passages; and could be differentiated into neurons, astrocytes, and oligodendrocytes, in addition to the brain region-specific neuronal subtypes GABAergic, dopaminergic, and motor neurons. Global gene expression of the transcripts of pNSCs was comparable to that of rosette-derived and human fetal-derived NSCs. This work demonstrates an efficient method to generate expandable pNSCs, which can be further differentiated into central nervous system neurons and glia with temporal, spatial, and positional cues of brain regional heterogeneity. This method of pNSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.