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Australia’s greener path in a competitive global lithium supply chain
Get PDFLithium is vital for the decarbonization transition. With Australian mines supplying over 50% of global demand, building greener lithium mining in Australia is essential. Therefore, this study conducts a site-specific assessment of all seven Australian mine sites in the latest decade, examining factors such as ore yield, grade, mining costs, and emission intensity. Our analysis reveals that while Greenbushes is the lowest emitter, its limited lifespan, along with the planned expansions of other sites that have higher emission intensities, can significantly increase greenhouse gas emissions from the lithium supply in the future. To address the challenges of emissions and fluctuating lithium prices, Australia must make a sustainable strategic shift toward greater involvement in the downstream supply chain, including refining and manufacturing. A regional comparison with the Lithium Triangle highlights Australia’s mining strength and potential to become a greener lithium producer by diversifying its energy mix with renewables, adopting advanced technologies for low-grade ore recovery, and implementing strong policy frameworks to support collaborative mid-sized and emerging projects. These approaches will strengthen Australia’s role in the decarbonization transition, environmentally and economically
In vitro plant spectral response reveals dust stress
No full textEarly-stage plant stress detection is a key measure for sustainable agriculture management. Mineral dust as an abiotic stressor affects the physical, chemical, and physiological characteristics of plants, which are linked to the plant's visible and near-infrared (VNIR) reflectance. However, considering the intensity of plant exposure to dust and associated spectral feedback remain unclear. This study investigates the effects of dust particles on the spectral properties of 11 plant species over the growing season by conducting an in-vitro experiment based on VNIR spectroscopy. The capabilities of machine learning algorithms based on VNIR data, including partial least-squares regression (PLSR) and support vector machine (SVM), were also evaluated for dust stress detection. Analyses show that increases in dust concentration lead to (i) reduction of leaf chlorophyll and water contents; (ii) increase of spectral reflectance at 450–490, 640–660, 1370–1450, and 1820–1940 nm; (iii) decrease of spectral reflectance at 530–590, 740–1200 nm; (iv) decrease the slope and height of the red edge; (v) red absorption feature (AF) became smaller and shifted towards shorter wavelength; (vi) reduction of area, width, and depth of AFs at 400–740, 1350–1450, and 1800–1900 nm; and (vii) shift of AF position at 400–740 nm towards shorter wavelength. The results show that, PLSR estimates dust concentration with an R² ranging from 0.83 to 0.95. Additionally, the SVM successfully distinguishes between dust-exposed and non-dust-exposed samples, achieving an overall accuracy of 80–96 %. The research reveals how mineral dust affects the spectral behavior of plants, providing a basis for early-stage dust stress detection through the combination of VNIR spectroscopy and machine learning. Leveraging the research findings, transition from laboratory spectroscopy to hyperspectral remote sensing imagery enables cost-effective and extensive spatiotemporal monitoring, facilitating timely protective measures to mitigate dust-induced damage to plants
Non-pharmacological Supportive Care Interventions during Immunotherapy for People with Cancer: A Systematic Scoping Review and Future Directions.
Get PDFPURPOSE: Immunotherapy has transformed cancer treatment and outcomes. Patients receiving immunotherapy often encounter immune-related and treatment-related adverse events, leading to substantial supportive care needs. Currently, no recommendations exist to guide the use of non-pharmacological supportive care interventions for people with cancer undergoing immunotherapy treatments. This review aims to summarise the available evidence regarding non-pharmacological supportive care strategies to inform future clinical management and research directions. METHODS: Six electronic databases (PubMed, CINAHL, EMBASE, PsycInfo, Web of Science and Scopus) were systematically searched for studies on non-pharmacological supportive care interventions for adults undergoing immunotherapy, published from October 2014 to October 2024. RESULTS: A total of 5383 studies were screened, with 14 meeting the inclusion criteria. Five were interventional studies and ten were observational. The interventional studies included three physical activity and exercise interventions, two dietary interventions, and one multimodal intervention. Most interventions were found to be feasible, acceptable, and demonstrate preliminary efficacy at improving quality of life, symptom burden, and clinical outcomes. Observational evidence demonstrated associations between physical activity and dietary factors and improved quality of life, reduced symptom burden, and improved clinical outcomes. CONCLUSION: Growing observational and preliminary interventional evidence suggests a multimodal supportive care intervention that includes regular symptom monitoring, dietary support and exercise to address the physical and psychosocial needs of cancer patients undergoing immunotherapy may be beneficial. However, further high-quality trials are needed to confirm their efficacy and inform clinical implementation
Decoding PM2.5 oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.
No full textPM2.5 oxidative potential (OP), a key driver of health risks, was investigated in Ningbo, China, using dual dithiothreitol (DTT) and ascorbic acid (AA) assays combined with machine learning (ML). This approach accounts for the complexity of interactions among key chemical drivers and accurately identifies chemical species and PM2.5 sources associated with OP - a critical gap in prior studies relying solely on correlation analysis and linear regression. Year-long PM2.5 samples revealed higher nighttime and summer OP (volume-based OP-DTTv and OP-AAv), linked to aerosol acidity and photochemical aging. Among six ML models, Extremely Randomized Trees (ERT) outperformed others by 9.5-30.7 %, identifying Cu, Fe, V, As, Co, Cd, NO3-, Ni, and quinones as primary OP drivers, with synergistic effects for most constituents except antagonistic Fe. Source apportionment attributed OP mainly to vehicular emissions (40 %), marine/sea salt (20 %), and secondary aerosols (16 %). Biomass burning, industry, and road dust contributed minimally. Results emphasize targeting quinones, traffic-related metals (Cu, V), and synergistic metal interactions to mitigate PM2.5 toxicity in coastal cities. The dual-assay ML framework provides actionable insights for prioritizing OP-driven regulation, particularly in regions blending anthropogenic and marine influences, to reduce oxidative stress-related health burdens
Modeling the Abrasive Index from Mineralogical and Calorific Properties Using Tree-Based Machine Learning: A Case Study on the KwaZulu-Natal Coalfield
Get PDFAccurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R2: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R2 = 0.92), outperforming RF (R2 = 0.85) and GBT (R2 = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features
What does it mean for early career teachers to be classroom ready?
No full textThis chapter discusses how teacher perceptions about early career teacher (ECT) qualities reflect conflicting attitudes towards the competencies and capacities needed for them to be ‘classroom ready’ at the start of their careers. Data collected from a small sample of participants as part of the second phase of the Delphi process implemented in the What’s the Evidence study is used to demonstrate the lack of agreement on what being classroom ready might mean for ECTs in Australia. Responses to a survey that portray contrasting opinions about indicators deemed ‘least essential’ for ECTs were open, axial, and selectively coded into themes related to teacher career development, the cerebral or crafted nature of teaching, and the interrelated nature of indicators of teacher quality. Although only seven qualities were discussed by the respondents, the comments made about the indicators of creativity, analytical, advocacy, agency, leadership, influence, and self-confidence raised broad issues related to the attributes, capacities, and behaviours required of ECTs. The results raise interesting questions about the impact on teacher identity as ECTs negotiate divergent conceptions of classroom readiness
Implementing an eHealth Model of Care for Pediatric Patients and Families at the End-of-Treatment for Acute Lymphoblastic Leukemia [EMERGE]: A Type 2 Non-randomized Hybrid Implementation-effectiveness Trial Study Protocol (Preprint)
No full text
Self‐Assembled Metal‐Polyphenol Colchicine Nanoparticles Targeting Oxidative Stress and Inflammation for Treatment of Atherosclerosis
No full textAbstract
Anti‐inflammatory colchicine therapy has emerged as a new era for atherosclerotic cardiovascular diseases. However, the therapeutic benefit of colchicine has not been clearly defined. Herein, we present a double coordination‐driven approach to fabricate a stable metal‐organic nano‐assembly of colchicine (COL‐TA‐Zn) by uniting the tropolone ring of colchicine (COL), phenolic groups of tannic acid (TA), and Zn
2+
ions. This design leverages the antioxidant and anti‐inflammatory properties of COL and TA to create a nanoscale platform capable of scavenging radicals and modulating inflammatory pathways. Through robust Zn
2+
coordination, the resulting COL‐TA‐Zn nanocomplexes exhibit enhanced stability under physiological conditions, ensuring efficient delivery and sustained bioactivity. In vitro assays confirm suppression of foam cell formation and multiple inflammatory mediators, suggesting significant potential for managing atherosclerosis by targeting both oxidative stress and inflammation. Intravenous administration of COL‐TA‐Zn in
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mice significantly reduces atherosclerotic plaque area, MMP‐9, TNF‐α, and reactive oxygen species (ROS) levels, thereby illustrating its superior anti‐atherosclerotic efficacy compared to COL alone. These findings highlight the promise of the dual coordination‐driven nanoplatform in cardiovascular disease treatment.
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