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The aim of this study was to report pulmonary function tests (PFTs) and clinician-reported and patient-reported quality-of-life (QoL) outcomes on a cohort of patients with non-small cell lung cancer (NSCLC) treated with SABR. A total of 119 patients with NSCLC were treated with SABR in the prospective cohort SSBROC study of patients with T1-T2N0M0 NSCLC. PFTs and QoL measures were obtained at baseline pretreatment and at 6-month intervals. Here we report on the 6- to 18-month time points. Analysis of covariance (ANCOVA) methods adjusting for baseline analyzed potential predictors on outcomes of PFTs and patient-reported dyspnea at 18 months. The only statistically significant decline in PFTs was seen in forced expiratory volume in 1 second (FEV 1) at 18 months post-SABR, with a decline of −0.11 L (P =.0087; 95% CI, −0.18 to −0.02). Of potential predictors of decline, only a 1-unit increase in smoking pack-years resulted in a −0.12 change in diffusing capacity for carbon monoxide (P =.026; 95% CI, −0.02 to −0.23) and a 0.003 decrease in FEV 1 (P =.026; 95% CI, −0.006 to −0.0004). For patient-reported outcomes, statistically significant worsening in both the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (QLQ-C30 Version 3) and the lung module (QLQ-LC13) dyspnea scores occurred at the 18-month time point, but not earlier. No potential predictors of worsening dyspnea were statistically significant. There was no statistically significant decline in clinician-reported outcomes or global QoL scores. We found a statistically significant decline in FEV 1 at 18 months posttreatment. Smoking pack-years was a predictor for decline in diffusing capacity for carbon monoxide and FEV 1 at 18 months. Worsening of patient-reported dyspnea scores was observed, consistent with the expected progression of lung comorbid disease. [ABSTRACT FROM AUTHOR]

Background: There is consistent evidence that suggests first year students are at risk of weight gain, but the reasons for this vulnerability are still unclear. This study aimed to explore whether the ability to regulate eating behaviours is a predictor of weight and dietary changes in first year undergraduate students.Methods: First year undergraduate students from universities situated in London were invited to complete a survey at the beginning of the academic year and at 6-month follow-up. Each survey included the Self-Regulation of Eating Behaviour Questionnaire, food frequency questions, socio-demographic questions and anthropometric questions. Linear and logistic regressions were performed to explore the associations between baseline eating self-regulatory skills and weight and dietary changes.Results: 481 first year undergraduate students took part in the study. Students who entered university with higher eating self-regulatory skills were more likely to maintain or achieve a higher fruit and vegetable (OR = 1.8, p = 0.007) and a lower sweet and salty snack (OR = 1.9, p = 0.001) intake over the course of the first 6 months in university. Higher baseline eating self-regulatory skills were also related to lower weight changes (β = -0.15, p = 0.018) and lower likelihood of gaining 5% initial body weight (OR = 0.52, p = 0.006) at 6-month. Additionally, self-regulatory skills moderated the relationship between baseline BMI and weight changes (β = -0.25, p ≤0.001) and between baseline BMI and 5% weight gain (OR = 0.82, p = 0.008).Conclusions: Starting university with higher eating self-regulatory skills may help students to maintain or achieve a healthy diet and protect them against substantial weight gain, especially among students with overweight. [ABSTRACT FROM AUTHOR]

Space borne radar altimeters often complement in-situ water levels to provide greater insights to hydrodynamic models in sparsely gauged catchments. However, 10-day or 35-day water levels derived from satellite radar altimetry are generally too infrequent for flood forecasting or hydrodynamic modelling purposes. This paper proposes a new approach to find daily water levels for areas where in-situ river heights are not available. The new approach is based on the relationship between river height and difference between daytime and night time land surface temperatures (LST). This relationship is first demonstrated using in-situ gauge data to explore appropriate statistical models to predict river height using LST differences. The approach is then applied to a number of virtual stations at the intersections between the Mekong River and the ground-tracks of Jason-2 satellite altimetry which gives 10-day water level time series. The LST difference from the thermal infrared (TIR) observations of Moderate Resolution Imaging Spectro-radiometer (MODIS) is shown to have a strong relationship with in-situ water levels and a good relationship with the Jason-2 water levels. The models included a simple linear regression model which was then extended to firstly include seasonal terms and secondly assimilate satellite altimetry data. A regression model tree (M5) was also considered but was found to be inferior to the seasonal linear model. The developed regression models were used to predict daily water levels to infill the 10-day Jason-2 altimeters. RMSE of modelled daily water levels at gauges is between 0.3 m to 0.6 m, while RMSE of modelled daily water levels without using in-situ data is higher varying from 1.4 m to 1.9 m. The temporal correlation between the modelled water levels using satellite altimeters at virtual stations and in-situ water levels at adjacent gauges ranged from 0.72 to 0.86. These results show the potential of the proposed approach to produce high temporal resolution water levels for flood models or other applications using only remotely sensed data. [ABSTRACT FROM AUTHOR]

Digital Elevation Models (DEMs) such as Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models (ASTER GDEM), or Shuttle Radar Topography Mission DEM (SRTM) are widely used in remote areas and non-industrial countries because of their availability rather than their accuracy. Although a global DEM can be considerably enhanced using additional reference information such as higher resolution DEMs or ground truth points, improving accuracy in areas without reference data remains a challenge. This paper develops an approach to improve the accuracy of the estimated topography by combining two complementary DEMs (ASTER GDEM 1 arc-second and SRTM DEM 1 arc-second) in regions missing reference data. The combination approach is based on formulating relationships between slopes and weights in sites with reference data. Then the developed relationships are applied to sites with similar geomorphology to determine the combination weight for each DEM without using reference data. The results indicate that combined DEMs offer significant improvements of 47% and 20% in mean bias over a mountainous site, and 16% and 58% at a low-relief site when compared with the SRTM and ASTER GDEM products, respectively. DEM-derived drainages were also found to be more accurate for the combined DEMs than the near-global DEMs in areas where reference data is not available. [ABSTRACT FROM AUTHOR]

This study assesses two remotely sensed soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), a sensor onboard the Global Change Observation Mission 1 — Water (GCOM-W1) that was launched in May 2012. The soil moisture products were retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm and the Land Parameter Retrieval Model (LPRM) developed by the VU University Amsterdam, in collaboration with the National Aeronautics and Space Administration (NASA). The two products are compared at the global scale. In addition, the products are evaluated against field measurements from 47 stations from the COsmic-ray Soil Moisture Observing System (COSMOS) network which are located in the United States (36 stations), Australia (7 stations), Europe (2 stations) and Africa (2 stations). After examining the retrieval algorithms, it is hypothesized that four factors, namely, physical surface temperatures, surface roughness, vegetation and ground soil wetness conditions, may affect the quality of soil moisture retrievals. From the inter-comparisons at the global scale, the correlations of the two products highlight differences in the representation of the seasonal cycle of soil moisture, with negative correlations found for several regions. Correlations of the anomaly time series were generally strong (R > 0.6) as a result of soil moisture sensitivity to external meteorological forcing and possibly also random noise in the satellite observations. Due to the inherent differences in spatial coverage and measurement scale of the COSMOS and satellite data, the comparisons in terms of correlation coefficients are the most reliable. It was found that both products show rapid decreases in correlation coefficients under low mean temperature (< 290 K), high mean EVI (> 0.3) and highly wetted conditions. These findings are further supported by the bias and RMSE estimates which show that JAXA has relatively better performance under dry conditions while the bias and RMSE of LPRM are generally smaller than JAXA, when considered against the four variables. These results provide information on appropriate parameterizations and model selection for the retrieval algorithms and a future research direction to improve the quality by leveraging the strengths of the JAXA and LPRM algorithms. With these, when a multi-year dataset is available, there will be more confidence in defining the seasonal cycle and the data can be decomposed to identify the anomalies where the bias is not relevant. [ABSTRACT FROM AUTHOR]

This work presents an open-source tool to predict natural system responses by transforming the frequency spectrum of predictor variables to create a response that better resembles observations. The R package, namely WAvelet System Prediction (WASP), is based on a discrete wavelet transform (DWT)-based variance transformation method. We further introduce the maximal overlap DWT (MODWT)-based variance transformation which allows the method to be used in forecasting applications. We also develop the method to include an unbiased estimator that mitigates the well-known issue of edge effects in wavelet transforms. The predictive model in the method is a k-nearest neighbor (knn) approach. The main functionalities of the software include: (1) transforming the system predictors, (2) identifying significant predictors corresponding to the response, (3) predicting target response using the knn. Results of predicting sustained drought anomalies across Australia show clear improvements in predictive skill compared to the use of untransformed predictors. • Open source R-package WASP for modelling and predicting natural system responses. • The package modulates the variance in wavelet transformations to improve the match between predictors and the response. • The inclusion of an alternative wavelet transform overcomes the issue of future dependence in discrete wavelet transform. • The approach is demonstrated with an application to characterise drought using climatic indicators across Australia. [ABSTRACT FROM AUTHOR]

• Use aggregated climate indices to improve prediction of sustained drought and wet anomalies in term of SPI. • Formulate multiple models considering predictors of each model as different as possible. • Quantification of uncertainty in predictions of drought and wet conditions. Long term skillful prediction of prolonged hydrologic anomalies is essential for proper planning to reduce the societal risk of extreme hydrological anomalies such as drought. Climate indices estimated from sea surface temperature anomalies (SSTA) of the Pacific and Indian Oceans are often used to predict monthly and seasonal rainfall in Australia and many other places around the world. This study investigates the merit of distorting the time aggregation of such indices before casting them in a predictive model. Aggregated climate indices are used to predict sustained drought and wet anomalies characterised here using a drought index (i.e. Standardize Precipitation Index, SPI) as response and the Australia as the study region of interest. The aim is to enhance the strength of relationships of drought index and climate indices (predictors) by tuning the frequency of climate predictors using an aggregation technique. Result shows that aggregated climate indices provide significant improvement in prediction of SPI over raw climate indices across Australia. As strong spatial variations in optimum aggregation window lengths are evident across Australia suggesting multiple candidate predictive models with similar accuracies, a model combination approach is also adopted. Model combination is found useful in reducing structural uncertainty and further improving the prediction efficiency. Given that the improved predictive accuracy for SSTA the current generation of climate models exhibit, the methodology developed in this study has significant implications for skillful prediction and projection of long term droughts. [ABSTRACT FROM AUTHOR]

• This study assesses the sensitivity of a routinely adopted detection methodology to model uncertainty and bias within a hydro-climatological context. • The extent of uncertainty (as measured by the variance) and the consistency of trend play a critical role in changing the detection outcome. • When trends are consistent, variance modulation through decadal averaging helps improve the D&A outcome. • With inconsistent trends between simulations and observations, modulating variance leads to high uncertainty in D&A analysis. Detection of systematic changes in the climate system resulting from anthropogenic forcing is a critical area of research. Detection and attribution of hydro-climatological change has been limited by model uncertainty and bias as well as the poor spatial-temporal coverage of observational data. This study assesses a routinely adopted detection methodology and its sensitivity to model uncertainty and bias within a hydro-climatological context. Using a synthetic case study, we establish the sensitivity of detection approaches to the magnitude and consistency of trend and variance along with the length of data available. It is found that the extent of uncertainty (as measured by the variance) plays a critical role in changing the detection outcome. Another important factor is the consistency of trend between simulations and observations. A case study of soil moisture in select locations within Australia shows that averaging over multiple years (e.g., five years to a decade) improves the detection of the climate change signal as long as consistency in the trends exists. Our results also demonstrate that there are substantial differences in simulated trends across climate models. Therefore, even though ensemble averaging is effective in modulating variance, it has the risk of canceling out the signal over models with markedly different responses. [ABSTRACT FROM AUTHOR]

• Periods of increased algal activity are costly for water authorities to manage. • Machine learning predictive models have poor skill in detecting rare events. • Resampling techniques improve both the structure and parameters of a Bayesian Network. • For the case study of a major waterbody in Sydney Australia, oversampling was the most successful resampling technique. Early warning of increased algal activity is important to mitigate potential impacts on aquatic life and human health. While many methods have been developed to predict increased algal activity, an ongoing issue is that severe algal blooms often occur with low frequency in water bodies. This results in imbalanced data sets available for model specification, leading to poor predictions of the frequency of increased algal activity. One approach to address this is to resample data sets of increased algal activity to increase the prevalence of higher than normal algal activity in calibration data and ultimately improve model predictions. This study aims to investigate the use of resampling techniques to address the imbalanced dataset and determine if such methods can improve the prediction of increased algal activity. Three techniques were investigated, Kmeans under-sampling (US_Kmeans), synthetic minority over-sampling technique (SMOTE), and 'SMOTE and cluster-based under-sampling technique' (SCUT). The resampling methods were applied to a Bayesian network (BN) model of Lake Burragorang in New South Wales, Australia. The model was developed to predict chlorophyll- a (chl- a) using a range of water quality parameters as predictors. The original data and each of the balanced datasets were used for BN structures and parameter learning. The results showed that the best graphical structure was obtained by adding synthetic data from SMOTE with the highest true positive rate (TPR) and area under the curve (AUC). When compared using a fixed graphical structure for the BN, all resampling techniques increased the ability of the BN to detect events with higher probability of increased algal activity. The resampling model results can also be used to better understand the most important influences on high chl- a concentrations and suggest future data collection and model development priorities. [Display omitted] [ABSTRACT FROM AUTHOR]

89Zr-labelled proteins are gaining importance in clinical research in a variety of diseases. To date, no clinical study has been reported that utilizes an automated approach for radiosynthesis of 89Zr-labelled radiopharmaceuticals. We aim to develop an automated method for the clinical production of 89Zr-labelled proteins and apply this method to Durvalumab, a monoclonal antibody targeting PD-L1 immune-checkpoint protein. PD-L1 expression is poorly understood and can be up-regulated over the course of chemo- and radiotherapy treatment. The ImmunoPET multicentre study aims to examine the dynamics of PD-L1 expression via 89Zr-Durvalumab PET imaging before, during, and after chemoradiotherapy. The developed automated technique will enable reproducible clinical production of [89Zr]Zr-DFOSq-Durvalumab for this study at three different sites. Conjugation of Durvalumab to H 3 DFOSqOEt was optimized for optimal chelator-to-antibody ratio. Automated radiolabelling of H 3 DFOSq-Durvalumab with zirconium-89 was optimized on the disposable cassette based iPHASE technologies MultiSyn radiosynthesizer using a modified cassette. Activity losses were tracked using a dose calibrator and minimized by optimizing fluid transfers, reaction buffer, antibody formulation additives and pH. The biological profile of the radiolabelled antibody was confirmed in vivo in PD-L1+ (HCC827) and PD-L1- (A549) murine xenografts. Clinical process validation and quality control were performed at three separate study sites to satisfy clinical release criteria. H 3 DFOSq-Durvalumab with an average CAR of 3.02 was obtained. Radiolabelling kinetics in succinate (20 mM, pH 6) were significantly faster when compared to HEPES (0.5 M, pH 7.2) with >90 % conversion observed after 15 min. Residual radioactivity in the 89Zr isotope vial was reduced from 24 % to 0.44 % ± 0.18 % (n = 7) and losses in the reactor vial were reduced from 36 % ± 6 % (n = 4) to 0.82 % ± 0.75 % (n = 4) by including a surfactant in the reaction and formulation buffers. Overall process yield was 75 % ± 6 % (n = 5) and process time was 40 min. Typically, 165 MBq of [89Zr]Zr-DFOSq-Durvalumab with an apparent specific activity of 315 MBq/mg ± 34 MBq/mg (EOS) was obtained in a volume of 3.0 mL. At end-of-synthesis (EOS), radiochemical purity and protein integrity were always >99 % and >96 %, respectively, and dropped to 98 % and 65 % after incubation in human serum for 7 days at 37 °C. Immunoreactive fraction in HEK293/PD-L1 cells was 83.3 ± 9.0 (EOS). Preclinical in vivo data at 144 h p.i. showed excellent SUV max in PD-L1+ tumour (8.32 ± 0.59) with a tumour-background ratio of 17.17 ± 3.96. [89Zr]Zr-DFOSq-Durvalumab passed all clinical release criteria at each study site and was deemed suitable for administration in a multicentre imaging trial. Fully automated production of [89Zr]Zr-DFOSq-Durvalumab for clinical use was achieved with minimal exposure to the operator. The cassette-based approach allows for consecutive productions on the same day and offers an alternative to currently used manual protocols. The method should be broadly applicable to other proteins and has the potential for clinical impact considering the growing number of clinical trials investigating 89Zr-labelled antibodies. [Display omitted] [ABSTRACT FROM AUTHOR]

While the value of correcting raw radar rainfall estimates using simultaneous ground rainfall observations is well known, approaches that use the complete record of both gauge and radar measurements to provide improved rainfall estimates are much less common. We present here two new approaches for estimating radar rainfall that are designed to address known limitations in radar rainfall products by using a relatively long history of radar reflectivity and ground rainfall observations. The first of these two approaches is a radar rainfall estimation algorithm that is nonparametric by construction. Compared to the traditional gauge adjusted parametric relationship between reflectivity (Z) and ground rainfall (R), the suggested new approach is based on a nonparametric radar rainfall estimation method (NPR) derived using the conditional probability distribution of reflectivity and gauge rainfall. The NPR method is applied to the densely gauged Sydney Terrey Hills radar network, where it reduces the RMSE in rainfall estimates by 10%, with improvements observed at 90% of the gauges. The second of the two approaches is a method to merge radar and spatially interpolated gauge measurements. The two sources of information are combined using a dynamic combinatorial algorithm with weights that vary in both space and time. The weight for any specific period is calculated based on the error covariance matrix that is formulated from the radar and spatially interpolated rainfall errors of similar reflectivity periods in a cross-validation setting. The combination method reduces the RMSE by about 20% compared to the traditional Z-R relationship method, and improves estimates compared to spatially interpolated point measurements in sparsely gauged areas. [ABSTRACT FROM AUTHOR]

Background: The value of screen detection and treatment of ductal carcinoma in situ (DCIS) is a matter of controversy. At present, the extent to which the diagnosis and treatment of DCIS could prevent the occurrence of invasive breast cancer in the future is not clear. We sought to estimate the association between detection of DCIS at screening and invasive interval cancers subsequent to the relevant screen.Methods: We obtained aggregate data for screen-detected cancers from 84 local screening units within 11 regional Quality Assurance Reference Centres in England, Wales, and Northern Ireland from the National Health Service Breast Screening Programme. Data for DCIS diagnoses were obtained for women aged 50-64 years who were invited to and attended mammographic breast screening from April 1, 2003, to March 31, 2007 (4 screening years). Patient-level data for interval cancer arising in the 36 months after each of these were analysed by Poisson regression with invasive interval cancer screen detection rate as the outcome variable; DCIS detection frequencies were fitted first as a continuous and then as a categorical variable. We repeated this analysis after adjustment with both small size and high-grade invasive screen-detected cancers.Findings: We analysed data for 5,243,658 women and on interval cancers occurring in the 36 months after the relevant screen. The average frequency of DCIS detected at screening was 1·60 per 1000 women screened (median 1·50 [unit range 0·54-3·56] [corrected to] per 1000 women). There was a significant negative association of screen-detected DCIS cases with the rate of invasive interval cancers (Poisson regression coefficient -0·084 [95% CI -0·13 to -0·03]; p=0·002). 90% of units had a DCIS detection frequency within the range of 1·00 to 2·22 per 1000 women; in these units, for every three screen-detected cases of DCIS, there was one fewer invasive interval cancer in the next 3 years. This association remained after adjustment for numbers of small screen-detected invasive cancers and for numbers of grade 3 invasive screen-detected cancers.Interpretation: The association between screen-detected DCIS and subsequent invasive interval cancers suggests that detection and treatment of DCIS is worthwhile in prevention of future invasive disease.Funding: UK Department of Health Policy Research Programme and NHS Cancer Screening Programmes. [ABSTRACT FROM AUTHOR]

Climate change is expected to increase the prevalence of water-borne diseases especially in developing countries. Climate-resilient drinking water supplies are critical to protect communities from faecal contamination and thus against increasing disease risks. However, no quantitative assessment exists for the impacts of short-term climate variability on faecal contamination at different drinking water sources in developing countries, while existing understanding remains largely conceptual. This critical gap limits the ability to predict drinking water quality under climate change or to recommend climate-resilient water sources for vulnerable communities. This study aims to provide such quantitative understanding by investigating the relationships between faecal contamination and short-term climate variability across different types of water sources. We collected a novel dataset with over 20 months' monitoring of weather, Escherichia coli (E. coli) and total coliforms, at 233 different water sources in three climatically different regions in Tanzania. We then took a rigorous statistical analysis with Bayesian hierarchical models, to relate both contamination occurrence and amount to climate variability. The model results explained the temporal variability in drinking water faecal contamination using climate predictors, and also revealed the climate sensitivity of faecal contamination for individual water sources. We found that: a) short-term climate variability and baseline contamination levels can explain about half the observed variability in faecal contamination (R2 ≥ 0.44); b) increased contamination was most consistently related to recent heavy rainfall and high temperature across different water sources; c) unimproved water sources such as the unprotected dug wells have substantially higher climate sensitivity. Based on these results, we can expect substantial increases in drinking water contamination risks across tropical Sub-Saharan Africa and South-East Asian developing countries under a warmer climate, which highlight the urgent need of protecting vulnerable communities from the severe climate impacts. Unlabelled Image • Drinking water and climate were sampled over 20 months in three Tanzanian regions. • Modelling quantified the impact of heavy rain on increasing contamination. • Climate and source type explained half the variability in faecal contamination. • Improved water sources are most climate resilient against faecal contamination. [ABSTRACT FROM AUTHOR]