Physiological Studies on Postharvest Leaf Blackening in Cut Flower Protea Species.

To develop an understanding of the physiology which results in postharvest leaf blackening, cut flower Protea species were studied in relation to changes in leaf carbohydrate metabolism, membrane deterioration, and oxidative enzyme activity. Rapid development of leaf blackening in the dark was correlated with low starch and sucrose concentrations. Postharvest decline in leaf carbohydrate status appears to be associated with inflorescence sink demand. Leaf blackening was reduced in the light and carbohydrate pools were maintained due to photosynthesis. In floral stems of P. neriifolia, P. Susannae x compacta and P. eximia (which have differing susceptibilities to leaf blackening), leaf blackening was highest in P. eximia and lowest in P. Susannae x compacta. Preharvest sucrose concentrations were inversely related to leaf blackening, however preharvest starch concentrations were not related to leaf blackening. Low daytime photosynthetic rates and stomatal closure suggest some Protea species may be CAM plants. Leaf blackening may be related more to inflorescence sink demand after harvest than preharvest carbohydrate reserves. In P. neriifolia, postharvest addition of 0.5% exogenous sugar or decapitation of the flowerhead did not delay leaf blackening. A 24-h 20% sucrose pulse reduced blackening to that of vegetative stems. Starch concentrations dropped ca. 70% within 24-h of harvest. Leaves with reduced leaf blackening maintained higher levels of starch and sucrose early in the postharvest period. Low levels of lipid peroxidation and oxidized glutathione concentrations indicated that membrane damage (reduced leaf antioxidant status) did not precede appearance of blackening symptoms. Polyphenol oxidase (PPO) and peroxidase (POD) activities in leaves of P. Susannae x compacta indicated that PPO activity in dark-held stems may not be related to leaf blackening. POD and phenolic concentrations related more closely with appearance of leaf blackening. Chlorophyll and protein concentrations suggested that blackening is not related to senescence, but may result from a wounding response

Drawing a Positive Mathematics Identity: Portrait of a Maths Person

In this poster we’ll share a powerful classroom activity we designed that aims to broaden students’ definition of what a maths person looks like. We implemented this activity with Black and Latinx middle school students. We noticed a significant difference in students’ portraits with a simple intervention, exposing students to mathematicians from cultures who have historically been marginalized from maths spaces, specifically Black and Latinx mathematicians. These findings suggest that being introduced to mathematicians of color can help broaden students’ perceptions of who belongs in maths spaces and support students’ development of the confidence and skills they need to succeed in maths class and beyond

Productivity Measurements of Two Waratah 234 Hydraulic Tree Harvesters in Radiata Pine in New Zealand

Two Waratah 234 single-grip harvester heads were assessed for productivity and log-processing accuracy while working in radiata pine clearfell operations in New Zealand forests. Estimated productivity, processing stockpiled trees into logs on a landing in a ground-based operation, was 77 m3 per productive machine hour (PMH) in an average tree size of 1.63 m3. In a yarder-based trial, processing trees into logs on a landing, productivity was 77 m3 / PMH in an average tree size of 3.1 m3. Length-measuring accuracy in a later trial was shown to be within ± 5 cm for 93% of logs. In a second ground-based trial, estimated productivity for an average extracted tree size of 1.95m3 was: - 51 trees/PMH (100m3) for felling, tree-length delimbing and bunching. - 73 trees/PMH (143m3) for tree-length delimbing (butt-first) and bunching of manually felled trees

Patterns of strategic control: an investigation of British, French and German retail banking practice

This study provides detailed evidence as to the current state of strategic control in European retail banking. In so doing, it also provides an unique insight into the rigour of the strategic management of the banks investigated. [Continues.

Inclusive School Community: Why is it so Complex?

This paper addresses the question: why is it so hard for school communities to respond to diversity in learners, staff and parents in inclusive ways? The authors draw on theory and recent professional experience in Queensland, Australia, to offer four guiding principles that address traditional assumptions about learning that result in inequality of opportunity and outcomes for students. The authors suggest these principles to support the development of a more inclusive school community: (1) develop a learning community incorporating a critical friend; (2) value and collaborate with parents and the broader community; (3) engage students as citizens in school review and develop¬ment; and (4) support teachers’ critical engagement with inclusive ideals and practices. The authors describe how the principles can work in concert in a school community

Faculty Recital, Justin McConchie, percussion

Faculty RecitalJustin McConchie, percussionTuesday, February 19, 2019 at 7pmSonia Vlahcevic Concert Hall / W.E. Singleton Center for the Performing Arts922 Park Avenue / Richmond, V

Real-time Prediction of the Risk of Hospital Readmissions

This study aims to identify predictors for patients likely to be readmitted to a hospital within 28 days of discharge and to develop and validate a prediction model for identifying patients at a high risk of readmission. Numerous attempts have been made to build similar predictive models. However, the majority of existing models suffer from at least one of the following shortcomings: the model is not based on Australian Health Data; the model uses insurance claim data, which would not be available in a real-time clinical setting; the model does not consider socio-demographic determinants of health, which have been demonstrated to be predictive of readmission risk; or the model is limited to a particular medical condition and is thus limited in scope. To address these shortcomings, we built several models to predict all-cause 28-day readmission risk and included Socio-economic Indexes for Areas (SEIFA) data as proxies for socio-demographic determinants of health. Additionally, instead of using insurance claims data, which could require several weeks to process, we built our models using data that is readily available during the inpatient stay or at the time of discharge. The set of default prediction models that were examined include logistic regression, elastic net, random forest and adaptive boosting (Ada Boost). This study examined A not for profit tertiary healthcare organisation from fiscal year 2012-2013 through fiscal year 2017-2018. The out-of-sample results show that all of the models performed similarly and adequately to predict readmission risk