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Tree shade, particularly shade that obscures direct sunlight near peak periods of midday solar exposure can have a pronounced effect on potentially harmful ultraviolet radiation, and in turn, strongly influence the maximum daily UV index (UVI). In this study, the seasonal influence of tree shade on the UVI is evaluated from 210 hemispherical sky view images collected alongside public walkways and footpaths from 10 residential Brisbane suburbs. The effective sidewalk UV index is calculated underneath planted tree canopies, adjacent residential gardens, buildings and background tree species. Results are presented with respect to seasonal variations in the diurnal solar elevation for each month of the year at Brisbane's latitude. The research also examines the total reduction in UVI due to the presence of individual tree species, showing reductions in the midday UVI of up to 91% of an equivalent unimpeded sky hemisphere when overhead tree canopies are present. Important footpath tree species for peak midday UVI mitigation include Pongamia pinnata, Xanthostemon chrysanthus, Senna siamea, and Libidibia ferrea. The planting and maintenance of existing tree species already growing alongside residential Brisbane streets will improve the shade characteristics of suburbs and enhance UV protection for local residents., (© 2024 The Author(s). Photochemistry and Photobiology published by Wiley Periodicals LLC on behalf of American Society for Photobiology.)

Background: Current evidence suggests that cortisol secreting adrenocortical carcinoma has worse prognosis compared to non-secreting adrenocortical carcinoma. However, the effect of other secretory subtypes is unknown., Methods: This multicenter study within the American-Australian-Asian Adrenal Alliance included adults with adrenocortical carcinoma (1997-2020). We compared overall survival and disease-free survival among cortisol secreting, mixed cortisol/androgen secreting, androgen secreting, and non-secreting adrenocortical carcinoma., Results: Of the 807 patients (mean age 50), 719 included in the secretory subtype analysis: 24.5% were cortisol secreting, 13% androgen secreting, 28% mixed cortisol/androgen, 32.5% non-secreting, and 2% were mineralocorticoid secreting. Median overall survival and disease-free survival for the entire cohort were 60 and 9 months, respectively. Median overall survival was 36 months for cortisol, 30 for mixed, 60 for androgen secreting, and 115 for non-secreting adrenocortical carcinoma, P < .01. Median disease-free survival was 7 months for cortisol, 8 for mixed, 10 for androgen, and 12 for non-secreting adrenocortical carcinoma, P = .06. On multivariable analysis of age, sex, Ki67%, secretory subtype, stage, resection, and adjuvant therapy, predictors of worse overall survival were older age, higher Ki67%, stage IV, mixed secreting, R1, and no adjuvant therapy, P < .05. On subgroup analysis of R0 resection, predictors of worse overall survival included older age and higher Ki67%. Ki67% ≥40, stage III and cortisol secretion were associated with worse disease-free survival., Conclusion: Mixed cortisol/androgen secreting adrenocortical carcinoma was associated with worse overall survival, while cortisol or androgen secreting alone were not. Notably, among patients after R0 resection, secretory subtype did not affect overall survival. Cortisol secreting adrenocortical carcinoma demonstrated worse disease-free survival. Ki67% remained a strong predictor of worse overall survival and disease-free survival independent of stage., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Forecasting river water levels or streamflow water levels (SWL) is vital to optimising the practical and sustainable use of available water resources. We propose a new deep learning hybrid model for SWL forecasting using convolutional neural networks (CNN), bi-directional long-short term memory (BiLSTM), and ant colony optimisation (ACO) with a two-phase decomposition approach at the 7-day, 14-day, and 28-day forecast horizons. The newly developed CBILSTM method is coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods to extract the most significant features within predictor variables to build a hybrid CVMD-CBiLSTM model. We integrate three distinct datasets (satellite-derived, climate mode indices, and ground-based meteorological observations) to improve the forecasting capability of the CVMD-CBiLSTM model, applied at nineteen different gauging stations in the Australian Murray River system. This proposed model returns a significantly accurate performance with ~98% of all prediction errors within less than ±0.020 m and a low relative root mean square of ~0.08%, demonstrating its superiority over several benchmark models. The results show that using the new hybrid deep learning algorithm with ACO feature selection can significantly improve the accuracy of forecasted river water levels, and therefore, the method is attractive for adopting remote sensing data to the model ground-based river flow for strategic water savings planning initiatives and dealing with climate change-induced extreme events such as drought events., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)