DNA-based detection and characterisation of strictly anaerobic beer-spoilage bacteria

Megasphaera cerevisiae, Pectinatus cerevisiiphilus, Pectinatus frisingensis, Selenomonas lacticifex, Zymophilus paucivorans and Zymophilus raffinosivorans are strictly anaerobic Gram-stain-negative bacteria that are able to spoil beer by producing off-flavours and turbidity. They have only been isolated from the beer production chain. The species are phylogenetically affiliated to the Sporomusa sub-branch in the class "Clostridia". Routine cultivation methods for detection of strictly anaerobic bacteria in breweries are time-consuming and do not allow species identification. The main aim of this study was to utilise DNA-based techniques in order to improve detection and identification of the Sporomusa sub-branch beer-spoilage bacteria and to increase understanding of their biodiversity, evolution and natural sources. Practical PCR-based assays were developed for monitoring of M. cerevisiae, Pectinatus species and the group of Sporomusa sub-branch beer spoilers throughout the beer production process. The developed assays reliably differentiated the target bacteria from other brewery-related microbes. The contaminant detection in process samples (10 1,000 cfu/ml) could be accomplished in 2 8 h. Low levels of viable cells in finished beer (≤10 cfu/100 ml) were usually detected after 1 3 d culture enrichment. Time saving compared to cultivation methods was up to 6 d. Based on a polyphasic approach, this study revealed the existence of three new anaerobic spoilage species in the beer production chain, i.e. Megasphaera paucivorans, Megasphaera sueciensis and Pectinatus haikarae. The description of these species enabled establishment of phenotypic and DNA-based methods for their detection and identification. The 16S rRNA gene based phylogenetic analysis of the Sporomusa sub-branch showed that the genus Selenomonas originates from several ancestors and will require reclassification. Moreover, Z. paucivorans and Z. raffinosivorans were found to be in fact members of the genus Propionispira. This relationship implies that they were carried to breweries along with plant material. The brewery-related Megasphaera species formed a distinct sub-group that did not include any sequences from other sources, suggesting that M. cerevisiae, M. paucivorans and M. sueciensis may be uniquely adapted to the brewery ecosystem. M. cerevisiae was also shown to exhibit remarkable resistance against many brewery-related stress conditions. This may partly explain why it is a brewery contaminant. This study showed that DNA-based techniques provide useful tools for obtaining more rapid and specific information about the presence and identity of the strictly anaerobic spoilage bacteria in the beer production chain than is possible using cultivation methods. This should ensure financial benefits to the industry and better product quality to customers. In addition, DNA-based analyses provided new insight into the biodiversity as well as natural sources and relations of the Sporomusa sub-branch bacteria. The data can be exploited for taxonomic classification of these bacteria and for surveillance and control of contaminations.

A simple, one-tube assay for the simultaneous detection and diagnosis of ten Australian poultry Eimeria

Coccidiosis is a costly worldwide enteric disease of chickens caused by parasites of the genus Eimeria. At present, there are seven described species that occur globally and a further three undescribed, operational taxonomic units (OTUs X, Y, and Z) that are known to infect chickens from Australia. Species of Eimeria have both overlapping morphology and pathology and frequently occur as mixed-species infections. This makes definitive diagnosis with currently available tests difficult and, to date, there is no test for the detection of the three OTUs. This paper describes the development of a PCR-based assay that is capable of detecting all ten species of Eimeria, including OTUs X, Y, and Z in field samples. The assay is based on a single set of generic primers that amplifies a single diagnostic fragment from the mitochondrial genome of each species. This one-tube assay is simple, low-cost, and has the capacity to be high throughput. It will therefore be of great benefit to the poultry industry for Eimeria detection and control, and the confirmation of identity and purity of vaccine strains.

Development of a Multiplexed Bead-Based Suspension Array for the Detection and Discrimination of Pospiviroid Plant Pathogens

Efficient and reliable diagnostic tools for the routine indexing and certification of clean propagating material are essential for the management of pospiviroid diseases in horticultural crops. This study describes the development of a true multiplexed diagnostic method for the detection and identification of all nine currently recognized pospiviroid species in one assay using Luminex bead-based suspension array technology. In addition, a new data-driven, statistical method is presented for establishing thresholds for positivity for individual assays within multiplexed arrays. When applied to the multiplexed array data generated in this study, the new method was shown to have better control of false positives and false negative results than two other commonly used approaches for setting thresholds. The 11-plex Luminex MagPlex-TAG pospiviroid array described here has a unique hierarchical assay design, incorporating a near-universal assay in addition to nine species-specific assays, and a co-amplified plant internal control assay for quality assurance purposes. All assays of the multiplexed array were shown to be 100% specific, sensitive and reproducible. The multiplexed array described herein is robust, easy to use, displays unambiguous results and has strong potential for use in routine pospiviroid indexing to improve disease management strategies.

Pythium soft rot of ginger: Detection and identification of the causal pathogens, and their control

Ginger is considered by many people to be the outstanding member among 1400 other species in the family Zingiberaceae. Not only it is a valuable spice used by cooks throughout the world to impart unique flavour to their dishes but it also has a long track record in some Chinese and Indian cultures for treating common human ailments such as colds and headaches. Ginger has recently attracted considerable attention for its anti-inflammatory, antibacterial and antifungal properties. However, ginger as a crop is also susceptible to at least 24 different plant pathogens, including viruses, bacteria, fungi and nematodes. Of these, Pythium spp. (within the kingdom Stramenopila, phyllum Oomycota) are of most concern because various species can cause rotting and yield loss on ginger at any of the growth stages including during postharvest storage. Pythium gracile was the first species in the genus to be reported as a ginger pathogen, causing Pythium soft rot disease in India in 1907. Thereafter, numerous other Pythium spp. have been recorded from ginger growing regions throughout the world. Today, 15 Pythium species have been implicated as pathogens of the soft rot disease. Because accurate identification of a pathogen is the cornerstone of effective disease management programs, this review will focus on how to detect, identify and control Pythium spp. in general, with special emphasis on Pythium spp. associated with soft rot on ginger.

Experimental investigation of cowl shape and location on inlet characteristics at hypersonic Mach number

An experimental study is presented to show the effect of the cowl location and shape on the shock interaction phenomena in the inlet region for a 2D, planar scramjet inlet model. Investigations include schlieren visualization around the cowl region and heat transfer rate measurement inside the inlet chamber.Both regular and Mach reflections are observed when the forebody ramp shock reflects from the cowl plate. Mach stem heights of 3.3 mm and 4.1 mm are measured in 18.5 mm and 22.7 mm high inlet chambers respecively. Increased heat transfer rate is measured at the same location of chamber for cowls of longer lenghs is indicating additional mass flow recovery by the inlet.

Semantic Classes in Topic Detection and Tracking

Topic detection and tracking (TDT) is an area of information retrieval research the focus of which revolves around news events. The problems TDT deals with relate to segmenting news text into cohesive stories, detecting something new, previously unreported, tracking the development of a previously reported event, and grouping together news that discuss the same event. The performance of the traditional information retrieval techniques based on full-text similarity has remained inadequate for online production systems. It has been difficult to make the distinction between same and similar events. In this work, we explore ways of representing and comparing news documents in order to detect new events and track their development. First, however, we put forward a conceptual analysis of the notions of topic and event. The purpose is to clarify the terminology and align it with the process of news-making and the tradition of story-telling. Second, we present a framework for document similarity that is based on semantic classes, i.e., groups of words with similar meaning. We adopt people, organizations, and locations as semantic classes in addition to general terms. As each semantic class can be assigned its own similarity measure, document similarity can make use of ontologies, e.g., geographical taxonomies. The documents are compared class-wise, and the outcome is a weighted combination of class-wise similarities. Third, we incorporate temporal information into document similarity. We formalize the natural language temporal expressions occurring in the text, and use them to anchor the rest of the terms onto the time-line. Upon comparing documents for event-based similarity, we look not only at matching terms, but also how near their anchors are on the time-line. Fourth, we experiment with an adaptive variant of the semantic class similarity system. The news reflect changes in the real world, and in order to keep up, the system has to change its behavior based on the contents of the news stream. We put forward two strategies for rebuilding the topic representations and report experiment results. We run experiments with three annotated TDT corpora. The use of semantic classes increased the effectiveness of topic tracking by 10-30\% depending on the experimental setup. The gain in spotting new events remained lower, around 3-4\%. The anchoring the text to a time-line based on the temporal expressions gave a further 10\% increase the effectiveness of topic tracking. The gains in detecting new events, again, remained smaller. The adaptive systems did not improve the tracking results.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Season of birth and location of residence: Defining the role of ultraviolet radiation exposure on childhood temperament and behaviour

This thesis added new insight to research knowledge about the role that season and ultraviolet radiation (UV) exposure during pregnancy has on children's temperament and behaviours, using a nation-wide longitudinal study. It was found that young children born in summer months are likely to have problematic behaviours. The thesis also found that summer-born children are likely to receive lowest levels of UV exposure during the gestational period. Finally, this work showed that low gestational UV exposure is associated with an increased risk of behavioural problems in children.

High-Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

Stationary processes for object detection and non-rigid structure-from-motion

Stationary processes are random variables whose value is a signal and whose distribution is invariant to translation in the domain of the signal. They are intimately connected to convolution, and therefore to the Fourier transform, since the covariance matrix of a stationary process is a Toeplitz matrix, and Toeplitz matrices are the expression of convolution as a linear operator. This thesis utilises this connection in the study of i) efficient training algorithms for object detection and ii) trajectory-based non-rigid structure-from-motion.

Block-based feature detection and matching for mosaicing of camera-captured document images

In this paper, we present a new feature-based approach for mosaicing of camera-captured document images. A novel block-based scheme is employed to ensure that corners can be reliably detected over a wide range of images. 2-D discrete cosine transform is computed for image blocks defined around each of the detected corners and a small subset of the coefficients is used as a feature vector A 2-pass feature matching is performed to establish point correspondences from which the homography relating the input images could be computed. The algorithm is tested on a number of complex document images casually taken from a hand-held camera yielding convincing results.

Detection and progression of chronic allograft nephropathy : A study based on protocol biopsies

Background. Kidney transplantation (KTX) is considered to be the best treatment of terminal uremia. Despite improvements in short-term graft survival, a considerable number of kidney allografts are lost due to the premature death of patients with a functional kidney and to chronic allograft nephropathy (CAN). Aim. To investigate the risk factors involved in the progression of CAN and to analyze diagnostic methods for this entity. Materials and methods. Altogether, 153 implant and 364 protocol biopsies obtained between June 1996 and April 2008 were analyzed. The biopsies were classified according to Banff ’97 and chronic allograft damage index (CADI). Immunohistochemistry for TGF-β1 was performed in 49 biopsies. Kidney function was evaluated by creatinine and/or cystatin C measurement and by various estimates of glomerular filtration rate (GFR). Demographic data of the donors and recipients were recorded after 2 years’ follow-up. Results. Most of the 3-month biopsies (73%) were nearly normal. The mean CADI score in the 6-month biopsies decreased significantly after 2001. Diastolic hypertension correlated with ΔCADI. Serum creatinine concentration at hospital discharge and glomerulosclerosis were risk factors for ΔCADI. High total and LDL cholesterol, low HDL and hypertension correlated with chronic histological changes. The mean age of the donors increased from 41 -52 years. Older donors were more often women who had died from an underlying disease. The prevalence of delayed graft function increased over the years, while acute rejections (AR) decreased significantly over the years. Sub-clinical AR was observed in 4% and it did not affect long-term allograft function or CADI. Recipients´ drug treatment was modified along the Studies, being mycophenolate mophetil, tacrolimus, statins and blockers of the renine-angiotensin-system more frequently prescribed after 2001. Patients with a higher ΔCADI had lower GFR during follow-up. CADI over 2 was best predicted by creatinine, although with modest sensitivity and specificity. Neither cystatin C nor other estimates of GFR were superior to creatinine for CADI prediction. Cyclosporine A toxicity was seldom seen. Low cyclosporin A concentration after 2 h correlated with TGF- β1 expression in interstitial inflammatory cells, and this predicted worse graft function. Conclusions. The progression of CAN has been affected by two major factors: the donors’ characteristics and the recipients’ hypertension. The increased prevalence of DGF might be a consequence of the acceptance of older donors who had died from an underlying disease. Implant biopsies proved to be of prognostic value, and they are essential for comparison with subsequent biopsies. The progression of histological damage was associated with hypertension and dyslipidemia. The augmented expression of TGF-β1 in inflammatory cells is unclear, but it may be related to low immunosuppression. Serum creatinine is the most suitable tool for monitoring kidney allograft function on every-day basis. However, protocol biopsies at 6 and 12 months predicted late kidney allograft dysfunction and affected the clinical management of the patients. Protocol biopsies are thus a suitable surrogate to be used in clinical trials and for monitoring kidney allografts.