Physiology and biochemistry of budburst in Vitis vinifera

Both the physiological and biochemical control of budburst in the grapevine, Vitis Vinifera L. were investigated. It was found that the accuracy of a predictive model for grapevine budburst based on ambient temperature was limited under the experimental conditions. There was a significant correlation of 4.7 ± 0.3 days between the days of maximal xylem exudation and budburst over the 3 years of investigation. The co-relationships between daily xylem exudate volume and a range of environmental parameters were considered. It was found that soil temperature was highly correlated against daily xylem exudation. Ambient temperature and soil moisture were significantly correlated with xylem exudation, however the coefficients of correlation were much lower than that of soil temperature. Rainfall showed only a very limited correlation with daily xylem exudate flow. Seasonal variations in the pH and the carbohydrate and inorganic nutrient concentrations of xylem exudate were investigated. Exudate carbohydrate concentrations fell from 660 µM before the day of maximal xylem exudation to zero levels within 4 weeks. Xylem exudate pH was found to consistently fall to a minimum at the time of maximal exudate flow. Exudate concentrations of the metallic cofactors Ca, K, Mg, Mn and Zn varied directly with daily exudate flow, suggesting some sort of flow-dependent mobilisation of these nutrients. A growth promontory oligosaccharide fraction was prepared by partial acid hydrolysis of grapevine primary cell wall material. This fraction significantly increased control growth of the Lemna minor L. bioassay over a limited ‘window’ of bioactivity. A growth inhibitory oligosaccharide fraction, similar in activity to abscisic acid was isolated from grapevine xylem exudate prior to budburst. The exudate concentration or efficacy of this substance declined after budburst such that there was no apparent growth inhibition. A model is proposed for grapevine budburst whereby an oligosaccharide growth inhibitor is gradually removed from the xylematic stream under the effects of soil temperature, allowing the surge of metabolic activity and vegetative growth that constitute budburst.

Study of the physiology and biochemistry of chlorophyllase in Zostera muelleri

Examines the biochemistry and physiology of chlorophyllase in the marine angiosperm Zostera muelleri. Enzyme activity was found to be highly correlated with chlorophyll synthesis in developing leaf tissue. This study provides evidence for the physiological role of chlorophyllase in chlorophyll degradation and provides insights into the senescence process in seagrass.

Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.