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Pastures are a primary food source for livestock in Australia, with various pasture species grown in rotations. Accurate prediction of pasture availability is critical for effective farm management, livestock growth, and maintaining the supply chain. Environmental factors, particularly climate, heavily influence pasture yield. However, different pasture species respond to climate attributes with varying time lags; for example, one species might be more influenced by last week’s weather while another by the previous month’s highlighting the nuanced temporal dependencies. This time-lagging effect complicates the development of machine-learning models that can learn the temporal dependencies to predict pasture yield. To address this, our study proposes an averaging-based feature engineering approach, effectively capturing the varying temporal dependencies across pasture species and also allowing interpretation of the dependencies. Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. Our results show that incorporating averaging-based feature-engineered climate attributes significantly improves pasture yield predictions, with enhancements of up to 20.28%, 31.81%, and 31.11% across the three evaluation measures, RMSE, MAE, and R2, respectively. This approach also enhances interpretability, revealing diverse time-lagging effects on different pasture species. XGBoost-based feature importance analysis further unveils insights into the influence of each climate attribute and its temporal dependencies on pasture yield.

We explore the discipline’s attempts to define design in extant literature and in an analysis of 20 years of discussion extracted from the PhD Design email list hosted by JISCMail. We describe in detail our novel method using data mining and cluster visualization, and we present and compare the topics of discussion that emerged from the 20-year data sample. We identified four highly influential term labels based on their common occurrence: theory, knowledge, activity, and science. Our analysis of the text associated with these terms revealed prevailing perspectives of what design is within the design research community conversation. Despite robust discourse around relevant perspectives on design, the list discussions are and have been repetitive, with no significant progress made towards a consolidated definition of design. We propose that it may not be possible to define design in this way, and that the field should move away from reiteration and discuss the importance of design’s role as a leader of the global conversation about transdisciplinary approaches to research and design of the future scenarios and emerging pathways of humankind that such pathways point to.

Appropriate descriptions of statistical methods are essential for evaluating research quality and reproducibility. Despite continued efforts to improve reporting in publications, inadequate descriptions of statistical methods persist. At times, reading statistical methods sections can conjure feelings of dèjá vu, with content resembling cut-and-pasted or “boilerplate text” from already published work. Instances of boilerplate text suggest a mechanistic approach to statistical analysis, where the same default methods are being used and described using standardized text. To investigate the extent of this practice, we analyzed text extracted from published statistical methods sections from PLOS ONE and the Australian and New Zealand Clinical Trials Registry (ANZCTR). Topic modeling was applied to analyze data from 111,731 papers published in PLOS ONE and 9,523 studies registered with the ANZCTR. PLOS ONE topics emphasized definitions of statistical significance, software and descriptive statistics. One in three PLOS ONE papers contained at least 1 sentence that was a direct copy from another paper. 12,675 papers (11%) closely matched to the sentence “a p-value < 0.05 was considered statistically significant”. Common topics across ANZCTR studies differentiated between study designs and analysis methods, with matching text found in approximately 3% of sections. Our findings quantify a serious problem affecting the reporting of statistical methods and shed light on perceptions about the communication of statistics as part of the scientific process. Results further emphasize the importance of rigorous statistical review to ensure that adequate descriptions of methods are prioritized over relatively minor details such as p-values and software when reporting research outcomes.

Following the near-total depletion of pancreatic beta-cells with streptozotocin (STZ), a partial recovery of beta-cell mass (BCM) can occur, in part due to the alpha- to beta-cell transdifferentiation with an intermediary insulin/glucagon bi-hormonal cell phenotype. However, human type 2 diabetes typically involves only a partial reduction in BCM and it is not known if recovery after therapeutic intervention involves islet cell transdifferentiation, or how this varies with age. Here, we used transgenic mouse models to examine if islet cell transdifferentiation contributes to BCM recovery following only a partial depletion of BCM. Cell lineage tracing was employed using Glucagon-Cre/yellow fluorescent protein (YFP) transgenic mice treated with STZ (25 mg/kg-neonates; 70 mg/kg-adults) or vehicle alone on 3 consecutive days. Mice were euthanized 2-30 days later with a prior glucose tolerance test on day 30, and immunofluorescence histology performed on the pancreata. Beta-cell abundance was reduced by 30-40% two days post STZ in both neonates and adults, and subsequently partially recovered in adult but not neonatal mice. Glucose tolerance recovered in adult females, but not in males or neonates. Bi-hormonal cell abundance increased 2-3-fold in STZ-treated mice vs. controls in both neonates and adults, as did transdifferentiated cells expressing insulin and the YFP lineage tag, but not glucagon. Transdifferentiated cell presence was an order of magnitude lower than that of bi-hormonal cells. We conclude that alpha- to beta-cell transdifferentiation occurs in mice following only a moderate depletion in BCM, and that this was accompanied by a partial recovery of BCM in adults.

Studies of parameters which affect cellular proliferation, cellular differentiation and cell-cell interactions influencing cell behaviour are of particular interest. They can be used to identify and characterise molecules which, through changes in gene expression, induce or inhibit cell proliferation, differentiation and movement. Such studies are crucial, not only in the context of understanding growth and development, but also in understanding the processes of wound-healing and regeneration, tumour invasion and metastasis. Here we present a summary of some cell culture models which we have developed for the study of the above-mentioned phenomena, together with their application to studies of the regulation of HGF/SF expression.