Background and Aims: As grapegrowers move to adapt to climate change, they need more detailed information on what cultivars to plant and where to plant them. The aims of this study were to understand how different cultivars in different regions are responding to changes in climate, in order to inform future cultivar selections. Methods and Results: Trends in the day of year maturity (DOYM) between 1999 and 2018 were analysed for 23 grape cultivars (covering at least 7 years) and four Victorian vineyard regions against vintage year, seasonal growing degree day (GDD Sep-Mar ) and Spring Index. In most cases there were significant trends in DOYM advancement as a function of GDD Sep-Mar and spring index. Temporal advancement of DOYM was more variable. One cultivar showed a significant advancement at two of three sites and another showed a significant delay. Different cultivars advanced DOYM at significantly different rates at a given site, later ripening cultivars advanced DOYM faster than earlier ripening cultivars and for a cultivar grown across several sites, the DOYM advancement was faster at cooler sites. Conclusions: Grapevine cultivars respond to warming temperature differently and the advancement of grape maturity is predicted to slow as temperature further increases. Significance of the Study: The study showed diversity in the phenological response of cultivars to temperature, which may be utilised to better adapt to climate change.

Background Preeclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality worldwide. Although predictive multiparametric screening is being developed, it is not applicable to nulliparous women, and is not applied to low-risk women. As PE is considered a heterogenous disorder, it is unlikely that any single multiparametric screening protocol containing a small group of biomarkers could have the required accuracy to predict all PE subgroups. Given the etiology of PE is complex and not fully understood, it begs the question, whether the search for biomarkers based on the predominant view of impaired placentation involving factors predominately implicated in angiogenesis and inflammation, has been too limiting. Here we highlight the enormous potential of state-of-the-art, high-throughput proteomics, to provide a comprehensive and unbiased approach to biomarker identification. Methods and findings Our literature search identified 1336 articles; after review, 45 studies with proteomic data from PE women that were eligible for inclusion. From 710 proteins with altered abundance, we identified 13 common circulating proteins, some of which had not been previously considered as prospective biomarkers of PE. An additional search of the literature for original publications testing any of the 13 common proteins using non-proteomic techniques was also undertaken. Strikingly, 9 of these common proteins had been independently evaluated in PE studies as potential biomarkers. Conclusion This study highlights the potential of using high-throughput data sets, which are comprehensive and without bias, to identify a profile of proteins that may improve predictions of PE and understanding of its etiology. We bring to the attention of the medical and research communities that the strengths and advantages of using data from high-throughput studies for biomarker discovery would be increased dramatically, if first and second trimester samples were collected for proteomics, and if standardized guidelines for patient reporting and data collection were implemented.

Background and Purpose: General medical patients often present to the hospital with medical, social, cognitive, and functional issues that may impact discharge destination. The aim of this study was to investigate the association between patient factors at hospital admission and discharge destination in general medical patients. Methods: This was a prospective, single-site observational study conducted on the general medical wards at a tertiary hospital. Inpatients admitted to the general medical unit and referred to physical therapy were included. Patients admitted from residential care were excluded. Main Outcome Measures: Data were collected a median of 2 days (interquartile range: 1-3) from hospital admission and included demographics, comorbidities (Charlson Comorbidity Index), premorbid physical function (Blaylock Risk Assessment Screening Score, BRASS), current function (de Morton Mobility Index, DEMMI and Alpha Functional Independence Measure, AlphaFIM), and cognition (Rowland Universal Dementia Assessment Scale, RUDAS). Results: Between July 2016 and August 2017, 417 patients were recruited (53% female, median age: 81 years (interquartile range: 76-86). Of these, 245 patients were discharged directly home; 172 were not discharged home of whom 140 were discharged to a subacute temporary facility providing further opportunity for therapy and discharge planning. Patients discharged directly home had higher functional, mobility, and cognitive scores. Data were partitioned into training, validation, and test sets to provide unbiased estimates of sensitivity, specificity, receiver operating characteristic curve, and area under the curve. Models best associated with discharge were “DEMMI and toilet transfers” (sensitivity 82.1%, specificity 66.2%, area under the curve 83.8%, 95% confidence interval: 76.4-91.2) and “AlphaFIM and walking independence” (sensitivity: 66.7%, specificity: 83.1%, area under the curve: 81.5, 95% confidence interval: 73.2-89.7). Conclusion: Two models were created that differentiated between discharge home and not home and had similar statistical measures of validity. Although the models require further validation, clinicians should consider whether identification of patients likely to be discharged home or not home is of greater relevance for their clinical setting.

Artificial intelligence and radiomics have the potential to revolutionise cancer prognostication and personalised treatment. Manual outlining of the tumour volume for extraction of radiomics features (RF) is a subjective process. This study investigates robustness of RF to inter-observer variation (IOV) in contouring in lung cancer. We utilised two public imaging datasets: ‘NSCLC-Radiomics’ and ‘NSCLC-Radiomics-Interobserver1’ (‘Interobserver’). For ‘NSCLC-Radiomics’, we created an additional set of manual contours for 92 patients, and for ‘Interobserver’, there were five manual and five semi-automated contours available for 20 patients. Dice coefficients (DC) were calculated for contours. 1113 RF were extracted including shape, first order and texture features. Intraclass correlation coefficient (ICC) was computed to assess robustness of RF to IOV. Cox regression analysis for overall survival (OS) was performed with a previously published radiomics signature. The median DC ranged from 0.81 (‘NSCLC-Radiomics’) to 0.85 (‘Interobserver’—semi-automated). The median ICC for the ‘NSCLC-Radiomics’, ‘Interobserver’ (manual) and ‘Interobserver’ (semi-automated) were 0.90, 0.88 and 0.93 respectively. The ICC varied by feature type and was lower for first order and gray level co-occurrence matrix (GLCM) features. Shape features had a lower median ICC in the ‘NSCLC-Radiomics’ dataset compared to the ‘Interobserver’ dataset. Survival analysis showed similar separation of curves for three of four RF apart from ‘original_shape_Compactness2’, a feature with low ICC (0.61). The majority of RF are robust to IOV, with first order, GLCM and shape features being the least robust. Semi-automated contouring improves feature stability. Decreased robustness of a feature is significant as it may impact upon the features’ prognostic capability.

Background and Aims The major phenological events, such as harvest, are critical periods in the wine business calendar requiring much planning and organisation of resources, yet anticipation of the timing of these events is still imprecise. The aims of this study were to better understand why grape maturity (defined here as the day of the year the grapes reached 11.5 Bé) is advancing, and how different cultivars and regions are responding to the seasonal temperature conditions. Methods and Results Trends in rate of ripening (Bé/day or Bé/°C day) and the day of year veraison (DOYV) were analysed at four Victorian vineyard regions and included 24 cultivars covering 20 years. There was a significant difference between cultivars in their rate of ripening with later ripening cultivars ripening more slowly (Bé/day). Higher yield slowed the rate of ripening (Bé/day), significantly at two vineyards. No significant temporal trends were observed for the rate of ripening nor for the interval between DOYV and day of year maturity (DOYM), as related to Vintage Year or Springtime Temperature (max), although these may become apparent with a longer series of data and resulting smaller confidence intervals. Different cultivars, however, had a significantly different rate of change for this interval over time, and higher yield was associated with a longer interval length. Day of year veraison advanced significantly as related to Springtime Temperature (max) at all vineyards, and at a significantly different rate for different cultivars at three of the four vineyards. There was a positive association between yield and DOYV. Conclusions These results suggest that the observed advancement of grape maturity can be explained by the advancement of veraison, rather than an increase in the rate of ripening, for these cultivars in these regions. Significance of the Study The study showed that there is existing cultivar diversity which, if better understood, could help better anticipate phenological timing, improve vineyard management and assist in adapting to climate change.