The influence of vegetation and soil type on the speciation of iron in soil water

Soluble organic matter derived from exotic Pinus species has been shown to form stronger complexes with iron (Fe) than that derived from most native Australian species. It has also been proposed that the establishment of exotic Pinus plantations in coastal southeast Queensland may have enhanced the solubility of Fe in soils by increasing the amount of organically complexed Fe, but this remains inconclusive. In this study we test whether the concentration and speciation of Fe in soil water from Pinus plantations differs significantly from soil water from native vegetation areas. Both Fe redox speciation and the interaction between Fe and dissolved organic matter (DOM) were considered; Fe - DOM interaction was assessed using the Stockholm Humic Model. Iron concentrations (mainly Fe 2+) were greatest in the soil waters with the greatest DOM content collected from sandy podosols (Podzols), where they are largely controlled by redox potential. Iron concentrations were small in soil waters from clay and iron oxide-rich soils, in spite of similar redox potentials. This condition is related to stronger sorption on to the reactive clay and iron oxide mineral surfaces in these soils, which reduces the amount of DOM available for electron shuttling and microbial metabolism, restricting reductive dissolution of Fe. Vegetation type had no significant influence on the concentration and speciation of iron in soil waters, although DOM from Pinus sites had greater acidic functional group site densities than DOM from native vegetation sites. This is because Fe is mainly in the ferrous form, even in samples from the relatively well-drained podosols. However, modelling suggests that Pinus DOM can significantly increase the amount of truly dissolved ferric iron remaining in solution in oxic conditions. Therefore, the input of ferrous iron together with Pinus DOM to surface waters may reduce precipitation of hydrous ferric oxides (ferrihydrite) and increase the flux of dissolved Fe out of the catchment. Such inputs of iron are most probably derived from podosols planted with Pinus.

A randomised controlled trial to prevent hospital readmissions and loss of functional ability in high risk older adults: a study protocol

Background Older people have higher rates of hospital admission than the general population and higher rates of readmission due to complications and falls. During hospitalisation, older people experience significant functional decline which impairs their future independence and quality of life. Acute hospital services comprise the largest section of health expenditure in Australia and prevention or delay of disease is known to produce more effective use of services. Current models of discharge planning and follow-up care, however, do not address the need to prevent deconditioning or functional decline. This paper describes the protocol of a randomised controlled trial which aims to evaluate innovative transitional care strategies to reduce unplanned readmissions and improve functional status, independence, and psycho-social well-being of community-based older people at risk of readmission. Methods/Design The study is a randomised controlled trial. Within 72 hours of hospital admission, a sample of older adults fitting the inclusion/exclusion criteria (aged 65 years and over, admitted with a medical diagnosis, able to walk independently for 3 meters, and at least one risk factor for readmission) are randomised into one of four groups: 1) the usual care control group, 2) the exercise and in-home/telephone follow-up intervention group, 3) the exercise only intervention group, or 4) the in-home/telephone follow-up only intervention group. The usual care control group receive usual discharge planning provided by the health service. In addition to usual care, the exercise and in-home/telephone follow-up intervention group receive an intervention consisting of a tailored exercise program, in-home visit and 24 week telephone follow-up by a gerontic nurse. The exercise only and in-home/telephone follow-up only intervention groups, in addition to usual care receive only the exercise or gerontic nurse components of the intervention respectively. Data collection is undertaken at baseline within 72 hours of hospital admission, 4 weeks following hospital discharge, 12 weeks following hospital discharge, and 24 weeks following hospital discharge. Outcome assessors are blinded to group allocation. Primary outcomes are emergency hospital readmissions and health service use, functional status, psychosocial well-being and cost effectiveness. Discussion The acute hospital sector comprises the largest component of health care system expenditure in developed countries, and older adults are the most frequent consumers. There are few trials to demonstrate effective models of transitional care to prevent emergency readmissions, loss of functional ability and independence in this population following an acute hospital admission. This study aims to address that gap and provide information for future health service planning which meets client needs and lowers the use of acute care services.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Isolation and functional characterisation of banana phytoene synthase genes as potential cisgenes

Carotenoids occur in all photosynthetic organisms where they protect photosystems from auto-oxidation, participate in photosynthetic energy-transfer and are secondary metabolites. Of the more than 600 known plant carotenoids, few can be converted into vitamin A by humans and so these pro-vitamin A carotenoids (pVAC) are important in human nutrition. Phytoene synthase (PSY) is a key enzyme in the biosynthetic pathway of pVACs and plays a central role in regulating pVAC accumulation in the edible portion of crop plants. Bananas are a major commercial crop and serve as a staple crop for more than 30 million people. There is natural variation in fruit pVAC content across different banana cultivars, but this is not well understood. Therefore, we isolated PSY genes from banana cultivars with relatively high (cv. Asupina) and low (cv. Cavendish) pVAC content. We provide evidence that PSY in banana is encoded by two paralogs (PSY1 and PSY2), each with a similar gene structure to homologous genes in other monocots. Further, we demonstrate that PSY2 is more highly expressed in fruit pulp compared to leaf. Functional analysis of PSY1 and PSY2 in rice callus and E. coli demonstrate that both genes encode functional enzymes, and that Asupina PSYs have approximately twice the enzymatic activity of the corresponding Cavendish PSYs. These results suggest that differences in PSY enzyme activity contribute significantly to the differences in Asupina and Cavendish fruit pVAC content. Importantly, Asupina PSY genes could potentially be used to generate new cisgenic or intragenic banana cultivars with enhanced pVAC content.

Near infrared spectroscopy for non-destructive evaluation of articular cartilage

There is a need for an accurate real-time quantitative system that would enhance decision-making in the treatment of osteoarthritis. To achieve this objective, significant research is required that will enable articular cartilage properties to be measured and categorized for health and functionality without the need for laboratory tests involving biopsies for pathological evaluation. Such a system would provide the capability of access to the internal condition of the cartilage matrix and thus extend the vision-based arthroscopy that is currently used beyond the subjective evaluation of surgeons. The system required must be able to non-destructively probe the entire thickness of the cartilage and its immediate subchondral bone layer. In this thesis, near infrared spectroscopy is investigated for the purpose mentioned above. The aim is to relate it to the structure and load bearing properties of the cartilage matrix to the near infrared absorption spectrum and establish functional relationships that will provide objective, quantitative and repeatable categorization of cartilage condition outside the area of visible degradation in a joint. Based on results from traditional mechanical testing, their innovative interpretation and relationship with spectroscopic data, new parameters were developed. These were then evaluated for their consistency in discriminating between healthy viable and degraded cartilage. The mechanical and physico-chemical properties were related to specific regions of the near infrared absorption spectrum that were identified as part of the research conducted for this thesis. The relationships between the tissue's near infrared spectral response and the new parameters were modeled using multivariate statistical techniques based on partial least squares regression (PLSR). With significantly high levels of statistical correlation, the modeled relationships were demonstrated to possess considerable potential in predicting the properties of unknown tissue samples in a quick and non-destructive manner. In order to adapt near infrared spectroscopy for clinical applications, a balance between probe diameter and the number of active transmit-receive optic fibres must be optimized. This was achieved in the course of this research, resulting in an optimal probe configuration that could be adapted for joint tissue evaluation. Furthermore, as a proof-of-concept, a protocol for obtaining the new parameters from the near infrared absorption spectra of cartilage was developed and implemented in a graphical user interface (GUI)-based software, and used to assess cartilage-on-bone samples in vitro. This conceptual implementation has been demonstrated, in part by the individual parametric relationship with the near infrared absorption spectrum, the capacity of the proposed system to facilitate real-time, non-destructive evaluation of cartilage matrix integrity. In summary, the potential of the optical near infrared spectroscopy for evaluating articular cartilage and bone laminate has been demonstrated in this thesis. The approach could have a spin-off for other soft tissues and organs of the body. It builds on the earlier work of the group at QUT, enhancing the near infrared component of the ongoing research on developing a tool for cartilage evaluation that goes beyond visual and subjective methods.

The iatrogenic reflex and belief that repression can coercively suppress asymmetric coercive violence : does very robust action by the government decrease functional support for insurgents?

Non-state insurgent actors are too weak to compel powerful adversaries to their will, so they use violence to coerce. A principal objective is to grow and sustain violent resistance to the point that it either militarily challenges the state, or more commonly, generates unacceptable political costs. To survive, insurgents must shift popular support away from the state and to grow they must secure it. State actor policies and actions perceived as illegitimate and oppressive by the insurgent constituency can generate these shifts. A promising insurgent strategy is to attack states in ways that lead angry publics and leaders to discount the historically established risks and take flawed but popular decisions to use repressive measures. Such decisions may be enabled by a visceral belief in the power of coercion and selective use of examples of where robust measures have indeed suppressed resistance. To avoid such counterproductive behaviours the cases of apparent 'successful repression' must be understood. This thesis tests whether robust state action is correlated with reduced support for insurgents, analyses the causal mechanisms of such shifts and examines whether such reduction is because of compulsion or coercion? The approach is founded on prior research by the RAND Corporation which analysed the 30 insurgencies most recently resolved worldwide to determine factors of counterinsurgent success. This new study first re-analyses their data at a finer resolution with new queries that investigate the relationship between repression and insurgent active support. Having determined that, in general, repression does not correlate with decreased insurgent support, this study then analyses two cases in which the data suggests repression seems likely to be reducing insurgent support: the PKK in Turkey and the insurgency against the Vietnamese-sponsored regime after their ousting of the Khmer Rouge. It applies 'structured-focused' case analysis with questions partly built from the insurgency model of Leites and Wolf, who are associated with the advocacy of US robust means in Vietnam. This is thus a test of 'most difficult' cases using a 'least likely' test model. Nevertheless, the findings refute the deterrence argument of 'iron fist' advocates. Robust approaches may physically prevent effective support of insurgents but they do not coercively deter people from being willing to actively support the insurgency.