Spatio-temporal patterns of herbage availability and livestock movements: A cross-border analysis in the Chinese-Mongolian Altay

Due to increasing population and the recent implementation of policies to intensify the use of land and water resources, the transhumant pastoral systems in the Chinese-Mongolian Altay-Dzungarian region are rapidly changing, leading to modifications of herd size, herd composition and spatial distribution of livestock grazing. This may have major consequences for the supply and quality of rangeland biomass. Despite similar topographic settings, the socio-political framework for Chinese and Mongolian pastoralists differs significantly, leading to differences in rangeland utilization. To substantiate these claims, the long-distance transhumance routes, frequency of pasture changes, daily grazing itineraries and size of pastures were recorded by means of GPS tracking of cattle and goats on 1,535 (China) and 1,396 (Mongolia) observation days. The status quo of the main seasonal pastures was captured by measuring the herbage offer and its nutritive value in 869 sampling spots. In the Altay-Dzungarian region, small ruminant herds covered up to 412 km (Mongolia) and grazed on up to nine pastures per year (China). In Mongolia, the herds’ average duration of stay at an individual pasture was longer than in China, particularly in spring and autumn. Herbage allowance at the onset of a grazing period (kg dry matter per sheep unit and day) ranged from 34/17 to 91/95 (China/Mongolia). Comparing crude protein and phosphorous concentrations of herbage, in China, the highest concentrations were measured for spring and summer pastures, whereas in Mongolia, the highest concentrations were determined for autumn and winter pastures. Based on our data, we conclude that regulation of animal numbers and access to pastures seemingly maintained pasture productivity in China, especially at high altitudes. However, this policy may prohibit flexible adaptation to sudden environmental constraints. In contrast, high stocking densities and grazing of pastures before flowering of herbaceous plants negatively affected rangeland productivity in Mongolia, especially for spring and summer pastures.

The impact of denture related disease on the oral microbiome of denture wearers

Advances in healthcare over the last 100 years has resulted in an ever increasing elderly population. This presents greater challenges for adequate systemic and oral healthcare delivery. With increasing age there is a natural decline in oral health, leading to the loss of teeth and ultimately for some having to wear denture prosthesis. It is currently estimated that approximately one fifth of the UK and US populations have some form of removable prosthesis. The microbiology of denture induced mucosal inflammation is a pivotal factor to consider in denture care management, similar to many other oral diseases of microbial influence, such as caries, gingivitis and periodontitis. Dentures support the growth of microbial biofilms, structures commonly known as denture plaque. Microbiologically, denture stomatitis (DS) is a disease primarily considered to be of yeast aetiology, with the literature disproportionately focussed on Candida spp. However, the denture surface is capable of carrying up to 1011 microbes per milligram, the majority of which are bacteria. Thus it is apparent that denture plaque is more diverse than we assume. There is a fundamental gap in our understanding of the bacterial composition of denture plaque and the role that they may play in denture related disease such as DS. This is categorised as inflammation of the oral mucosa, a disease affecting around half of all denture wearers. It has been proposed that bacteria and fungi interact on the denture surface and that these polymicrobial interactions lead to synergism and increased DS pathogenesis. Therefore, understanding the denture microbiome composition is the key step to beginning to understand disease pathogenesis, and ultimately help improve treatments and identify novel targets for therapeutic and preventative strategies. A group of 131 patients were included within this study in which they provided samples from their dentures, palatal mucosa, saliva and dental plaque. Microbes residing on the denture surface were quantified using standard Miles and Misra culture technique which investigated the presence of Candida, aerobes and anaerobes. These clinical samples also underwent next generation sequencing using the Miseq Illumina platform to give a more global representation of the microbes present at each of these sites in the oral cavity of these denture wearers. This data was then used to compare the composition and diversity of denture, mucosal and dental plaque between one another, as well as between healthy and diseased individuals. Additional comparisons included denture type and the presence or absence of natural teeth. Furthermore, microbiome data was used to assess differences between patients with varying levels of oral hygiene. The host response to the denture microbiome was investigated by screening the patients saliva for the presence and quantification of a range of antimicrobial peptides that are associated with the oral cavity. Based on the microbiome data an in vitro biofilm model was developed that reflected the composition of denture plaque. These biofilms were then used to assess quantitative and compositional changes over time and in response to denture cleansing treatments. Finally, the systemic implications of denture plaque were assessed by screening denture plaque samples for the presence of nine well known respiratory pathogens using quantitative PCR. The results from this study have shown that the bacterial microbiome composition of denture wearers is not consistent throughout the mouth and varies depending on sample site. Moreover, the presence of natural dentition has a significant impact on the microbiome composition. As for healthy and diseased patients the data suggests that compositional changes responsible for disease progression are occurring at the mucosa, and that dentures may in fact be a reservoir for these microbes. In terms of denture hygiene practices, sleeping with a denture in situ was found to be a common occurrence. Furthermore, significant shifts in denture microbiome composition were found in these individuals when compared to the denture microbiome of those that removed their denture at night. As for the host response, some antimicrobial peptides were found to be significantly reduced in the absence of natural dentition, indicating that the oral immune response is gradually impaired with the loss of teeth. This study also identified potentially serious systemic implications in terms of respiratory infection, as 64.6% of patients carried respiratory pathogens on their denture. In conclusion, this is the first study to provide a detailed understanding of the oral microbiome of denture wearers, and has provided evidence that DS development is more complex than simply a candidal infection. Both fungal and bacterial kingdoms clearly play a role in defining the progression of DS. The biofilm model created in this study demonstrated its potential as a platform to test novel actives. Future use of this model will aid in greater understanding of host: biofilm interactions. Such findings are applicable to oral health and beyond, and may help to identify novel therapeutic targets for the treatment of DS and other biofilm associated diseases.

Endemic zoonoses: a one health approach

Endemic zoonotic diseases remain a serious but poorly recognised problem in affected communities in developing countries. Despite the overall burden of zoonoses on human and animal health, information about their impacts in endemic settings is lacking and most of these diseases are continuously being neglected. The non-specific clinical presentation of these diseases has been identified as a major challenge in their identification (even with good laboratory diagnosis), and control. The signs and symptoms in animals and humans respectively, are easily confused with other non-zoonotic diseases, leading to widespread misdiagnosis in areas where diagnostic capacity is limited. The communities that are mostly affected by these diseases live in close proximity with their animals which they depend on for livelihood, which further complicates the understanding of the epidemiology of zoonoses. This thesis reviewed the pattern of reporting of zoonotic pathogens that cause febrile illness in malaria endemic countries, and evaluates the recognition of animal associations among other risk factors in the transmission and management of zoonoses. The findings of the review chapter were further investigated through a laboratory study of risk factors for bovine leptospirosis, and exposure patterns of livestock coxiellosis in the subsequent chapters. A review was undertaken on 840 articles that were part of a bigger review of zoonotic pathogens that cause human fever. The review process involves three main steps: filtering and reference classification, identification of abstracts that describe risk factors, and data extraction and summary analysis of data. Abstracts of the 840 references were transferred into a Microsoft excel spread sheet, where several subsets of abstracts were generated using excel filters and text searches to classify the content of each abstract. Data was then extracted and summarised to describe geographical patterns of the pathogens reported, and determine the frequency animal related risk factors were considered among studies that investigated risk factors for zoonotic pathogen transmission. Subsequently, a seroprevalence study of bovine leptospirosis in northern Tanzania was undertaken in the second chapter of this thesis. The study involved screening of serum samples, which were obtained from an abattoir survey and cross-sectional study (Bacterial Zoonoses Project), for antibodies against Leptospira serovar Hardjo. The data were analysed using generalised linear mixed models (GLMMs), to identify risk factors for cattle infection. The final chapter was the analysis of Q fever data, which were also obtained from the Bacterial Zoonoses Project, to determine exposure patterns across livestock species using generalized linear mixed models (GLMMs). Leptospira spp. (10.8%, 90/840) and Rickettsia spp. (10.7%, 86/840) were identified as the most frequently reported zoonotic pathogens that cause febrile illness, while Rabies virus (0.4%, 3/840) and Francisella spp. (0.1%, 1/840) were least reported, across malaria endemic countries. The majority of the pathogens were reported in Asia, and the frequency of reporting seems to be higher in areas where outbreaks are mostly reported. It was also observed that animal related risk factors are not often considered among other risk factors for zoonotic pathogens that cause human fever in malaria endemic countries. The seroprevalence study indicated that Leptospira serovar Hardjo is widespread in cattle population in northern Tanzania, and animal husbandry systems and age are the two most important risk factors that influence seroprevalence. Cattle in the pastoral systems and adult cattle were significantly more likely to be seropositive compared to non-pastoral and young animals respectively, while there was no significant effect of cattle breed or sex. Exposure patterns of Coxiella burnetii appear different for each livestock species. While most risk factors were identified for goats (such as animal husbandry systems, age and sex) and sheep (animal husbandry systems and sex), there were none for cattle. In addition, there was no evidence of a significant influence of mixed livestock-keeping on animal coxiellosis. Zoonotic agents that cause human fever are common in developing countries. The role of animals in the transmission of zoonotic pathogens that cause febrile illness is not fully recognised and appreciated. Since Leptospira spp. and C. burnetii are among the most frequently reported pathogens that cause human fever across malaria endemic countries, and are also prevalent in livestock population, control and preventive measures that recognise animals as source of infection would be very important especially in livestock-keeping communities where people live in close proximity with their animals.

Monitoring animal behaviour and environmental interactions : GPS collars and satellite remote sensing within a satellite remote sensing context

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Animal behaviour understanding using wireless sensor networks

This paper presents research that is being conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) with the aim of investigating the use of wireless sensor networks for automated livestock monitoring and control. It is difficult to achieve practical and reliable cattle monitoring with current conventional technologies due to challenges such as large grazing areas of cattle, long time periods of data sampling, and constantly varying physical environments. Wireless sensor networks bring a new level of possibilities into this area with the potential for greatly increased spatial and temporal resolution of measurement data. CSIRO has created a wireless sensor platform for animal behaviour monitoring where we are able to observe and collect information of animals without significantly interfering with them. Based on such monitoring information, we can identify each animal's behaviour and activities successfully

Rice Tungro Bacilliform

Many well-known specialists have contributed to this book which presents for the first time an in-depth look at the viruses, their satellites and the retrotransposons infecting (or occuring in) one plant family: the Poaceae (Gramineae). After molecular and biological descriptions of the viruses to species level, virus diseases are presented by crop: barley, maize, rice, rye, sorghum, sugarcane, triticales, wheats, forage, ornamental and lawn. A detailed index of the viruses and taxonomic lists will help readers in the search for information.

Evaluating Two Mass Screening Methods for Tungro Disease Resistance

Tungro is one of the most destructive viral diseases of rice in South and Southeast Asia. It is associated with two viruses---rice tungro bacilliform virus (RTBV) ,and rice tungro spherical virus (RTSV) (Hibino et al 1978). Both viruses are transmitted by the green leafhopper (GLH) Nephotettix virescens (Ling 1979), However, prior acquisition of RTSV is required for Ihe transmission of RTBV alone (Hibino 1983). Plants infected with both viruses show severe stunting and yellowing. Those infected with RTBV alone show mild stunting but no leaf discoloration whereas those infected with RTSV alone do not show any apparent symptoms (Hibino el al 1978). Since the late 1960s, tungro has been mainly managed through varietal resistance (Khush 1989). The instability of resistant varieties in the field (Dahal et .a1 1990) led to a reexamination of the nature of the incorporated sources of resistance and to the adoption of more precise and more accurate screening methods.

Apoptosis-related genes confer resistance to Fusarium wilt in transgenic ‘Lady Finger’ bananas

Fusarium wilt, caused by Fusarium oxysporum f. sp. cubense (Foc), is one of the most devastating diseases of banana (Musa spp.). Apart from resistant cultivars, there are no effective control measures for the disease. We investigated whether the transgenic expression of apoptosis-inhibition related genes in banana could be used to confer disease resistance. Embryogenic cell suspensions of the banana cultivar, ‘Lady Finger’, were stably transformed with animal genes that negatively regulate apoptosis, namely Bcl-xL, Ced-9 and Bcl-2 3’ UTR, and independently transformed plant lines were regenerated for testing. Following a 12 week exposure to Foc race 1 in small-plant glasshouse bioassays, seven transgenic lines (2 x Bcl-xL, 3 x Ced-9 and 2 x Bcl-2 3’ UTR) showed significantly less internal and external disease symptoms than the wild-type susceptible ‘Lady Finger’ banana plants used as positive controls. Of these, one Bcl-2 3’ UTR line showed resistance that was equivalent to that of wild-type Cavendish bananas that were included as resistant negative controls. Further, the resistance of this line continued for 23 weeks post-inoculation at which time the experiment was terminated. Using TUNEL assays, Foc race 1 was shown to induce apoptosis-like features in the roots of wild-type ‘Lady Finger’ plants consistent with a necrotrophic phase in the lifecycle of this pathogen. This was further supported by the observed reduction of these effects in the roots of the resistant Bcl-2 3’ UTR transgenic line. This is the first report on the generation of transgenic banana plants with resistance to Fusarium wilt.

Bayesian algorithms with applications

Advances in algorithms for approximate sampling from a multivariable target function have led to solutions to challenging statistical inference problems that would otherwise not be considered by the applied scientist. Such sampling algorithms are particularly relevant to Bayesian statistics, since the target function is the posterior distribution of the unobservables given the observables. In this thesis we develop, adapt and apply Bayesian algorithms, whilst addressing substantive applied problems in biology and medicine as well as other applications. For an increasing number of high-impact research problems, the primary models of interest are often sufficiently complex that the likelihood function is computationally intractable. Rather than discard these models in favour of inferior alternatives, a class of Bayesian "likelihoodfree" techniques (often termed approximate Bayesian computation (ABC)) has emerged in the last few years, which avoids direct likelihood computation through repeated sampling of data from the model and comparing observed and simulated summary statistics. In Part I of this thesis we utilise sequential Monte Carlo (SMC) methodology to develop new algorithms for ABC that are more efficient in terms of the number of model simulations required and are almost black-box since very little algorithmic tuning is required. In addition, we address the issue of deriving appropriate summary statistics to use within ABC via a goodness-of-fit statistic and indirect inference. Another important problem in statistics is the design of experiments. That is, how one should select the values of the controllable variables in order to achieve some design goal. The presences of parameter and/or model uncertainty are computational obstacles when designing experiments but can lead to inefficient designs if not accounted for correctly. The Bayesian framework accommodates such uncertainties in a coherent way. If the amount of uncertainty is substantial, it can be of interest to perform adaptive designs in order to accrue information to make better decisions about future design points. This is of particular interest if the data can be collected sequentially. In a sense, the current posterior distribution becomes the new prior distribution for the next design decision. Part II of this thesis creates new algorithms for Bayesian sequential design to accommodate parameter and model uncertainty using SMC. The algorithms are substantially faster than previous approaches allowing the simulation properties of various design utilities to be investigated in a more timely manner. Furthermore the approach offers convenient estimation of Bayesian utilities and other quantities that are particularly relevant in the presence of model uncertainty. Finally, Part III of this thesis tackles a substantive medical problem. A neurological disorder known as motor neuron disease (MND) progressively causes motor neurons to no longer have the ability to innervate the muscle fibres, causing the muscles to eventually waste away. When this occurs the motor unit effectively ‘dies’. There is no cure for MND, and fatality often results from a lack of muscle strength to breathe. The prognosis for many forms of MND (particularly amyotrophic lateral sclerosis (ALS)) is particularly poor, with patients usually only surviving a small number of years after the initial onset of disease. Measuring the progress of diseases of the motor units, such as ALS, is a challenge for clinical neurologists. Motor unit number estimation (MUNE) is an attempt to directly assess underlying motor unit loss rather than indirect techniques such as muscle strength assessment, which generally is unable to detect progressions due to the body’s natural attempts at compensation. Part III of this thesis builds upon a previous Bayesian technique, which develops a sophisticated statistical model that takes into account physiological information about motor unit activation and various sources of uncertainties. More specifically, we develop a more reliable MUNE method by applying marginalisation over latent variables in order to improve the performance of a previously developed reversible jump Markov chain Monte Carlo sampler. We make other subtle changes to the model and algorithm to improve the robustness of the approach.

Cardiomyopathy classification : ongoing debate in the genomics era

Cardiomyopathies represent a group of diseases of the myocardium of the heart and include diseases both primarily of the cardiac muscle and systemic diseases leading to adverse effects on the heart muscle size, shape, and function. Traditionally cardiomyopathies were defined according to phenotypical appearance. Now, as our understanding of the pathophysiology of the different entities classified under each of the different phenotypes improves and our knowledge of the molecular and genetic basis for these entities progresses, the traditional classifications seem oversimplistic and do not reflect current understanding of this myriad of diseases and disease processes. Although our knowledge of the exact basis of many of the disease processes of cardiomyopathies is still in its infancy, it is important to have a classification system that has the ability to incorporate the coming tide of molecular and genetic information. This paper discusses how the traditional classification of cardiomyopathies based on morphology has evolved due to rapid advances in our understanding of the genetic and molecular basis for many of these clinical entities.

Book review: 'The Cornea' edited by H.E. Kaufman, B.A. Baron, M.B. McDonald and S.R. Waltman.

Parsons' Diseases of the Eye, first published in 1907, is one of the foundation texts of modern ophthalmology. It has seen a new edition at approximately 5-year intervals throughout the century. This latest edition incorporates developments that have taken place within the specialty since the 1984 impression, but remains in a virtually unchanged format...

Greenhouse gas emissions in livestock production systems

Agriculture is responsible for a significant proportion of total anthropogenic greenhouse gas emissions (perhaps 18% globally), and therefore has the potential to contribute to efforts to reduce emissions as a means of minimising the risk of dangerous climate change. The largest contributions to emissions are attributed to ruminant methane production and nitrous oxide from animal waste and fertilised soils. Further, livestock, including ruminants, are an important component of global and Australian food production and there is a growing demand for animal protein sources. At the same time as governments and the community strengthen objectives to reduce greenhouse gas emissions, there are growing concerns about global food security. This paper provides an overview of a number of options for reducing methane and nitrous oxide emissions from ruminant production systems in Australia, while maintaining productivity to contribute to both objectives. Options include strategies for feed modification, animal breeding and herd management, rumen manipulation and animal waste and fertiliser management. Using currently available strategies, some reductions in emissions can be achieved, but practical commercially available techniques for significant reductions in methane emissions, particularly from extensive livestock production systems, will require greater time and resource investment. Decreases in the levels of emissions from these ruminant systems (i.e., the amount of emissions per unit of product such as meat) have already been achieved. However, the technology has not yet been developed for eliminating production of methane from the rumen of cattle and sheep digesting the cellulose and lignin-rich grasses that make up a large part of the diet of animals grazing natural pastures, particularly in arid and semi-arid grazing lands. Nevertheless, the abatement that can be achieved will contribute significantly towards reaching greenhouse gas emissions reduction targets and research will achieve further advances.