Do Phosphine Resistance Genes Influence Movement and Dispersal Under Starvation?

Phosphine resistance alleles might be expected to negatively affect energy demanding activities such as walking and flying, because of the inverse relationship between phosphine resistance and respiration. We used an activity monitoring system to quantify walking of Rhyzopertha dominica (F.) and a flight chamber to estimate their propensity for flight initiation. No significant difference in the duration of walking was observed between the strongly resistant, weakly resistant, and susceptible strains of R. dominica we tested, and females walked significantly more than males regardless of genotype. The walking activity monitor revealed no pattern of movement across the day and no particular time of peak activity despite reports of peak activity of R. dominica and Tribolium castaneum (Herbst) under field conditions during dawn and dusk. Flight initiation was significantly higher for all strains at 28 degrees C and 55% relative humidity than at 25, 30, 32, and 35 degrees C in the first 24 h of placing beetles in the flight chamber. Food deprivation and genotype had no significant effect on flight initiation. Our results suggest that known resistance alleles in R. dominica do not affect insect mobility and should therefore not inhibit the dispersal of resistant insects in the field.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Environmental factors affecting the occurrence of periglacial landforms in Finnish Lapland: a numerical approach

Determination of the environmental factors controlling earth surface processes and landform patterns is one of the central themes in physical geography. However, the identification of the main drivers of the geomorphological phenomena is often challenging. Novel spatial analysis and modelling methods could provide new insights into the process-environment relationships. The objective of this research was to map and quantitatively analyse the occurrence of cryogenic phenomena in subarctic Finland. More precisely, utilising a grid-based approach the distribution and abundance of periglacial landforms were modelled to identify important landscape scale environmental factors. The study was performed using a comprehensive empirical data set of periglacial landforms from an area of 600 km2 at a 25-ha resolution. The utilised statistical methods were generalized linear modelling (GLM) and hierarchical partitioning (HP). GLMs were used to produce distribution and abundance models and HP to reveal independently the most likely causal variables. The GLM models were assessed utilising statistical evaluation measures, prediction maps, field observations and the results of HP analyses. A total of 40 different landform types and subtypes were identified. Topographical, soil property and vegetation variables were the primary correlates for the occurrence and cover of active periglacial landforms on the landscape scale. In the model evaluation, most of the GLMs were shown to be robust although the explanation power, prediction ability as well as the selected explanatory variables varied between the models. The great potential of the combination of a spatial grid system, terrain data and novel statistical techniques to map the occurrence of periglacial landforms was demonstrated in this study. GLM proved to be a useful modelling framework for testing the shapes of the response functions and significances of the environmental variables and the HP method helped to make better deductions of the important factors of earth surface processes. Hence, the numerical approach presented in this study can be a useful addition to the current range of techniques available to researchers to map and monitor different geographical phenomena.

Studies on the mechanism of follitropin action at the cellular level

With a view toward understanding better the mechanism of action of follitropin, an attempt was made using granulosa cells obtained from intact immature estrogenized rats to study in short-term incubations the effect of highly purified ovine follitropin on the binding of the hormone to the cells and the associated aromatase response. A modified radioimmunoassay procedure has been used to monitor unlabeled physiologically fully active follitropin bound to the cell. A linear relationship between the actual amount of hormone bound to the cells and the estradiol produced in vitro has been established. The amount of ovine follitropin bound that can elicit a half-maximal response in estrogen production was calculated to be 400 pg. The number of follitropin binding sites per cell was 375 and the Kd of binding was 3.03 × 10−10 Image . By the addition of ovine follitropin antiserum at different time points of a 4-h incubation period, a continual need for follitropin support for estradiol production has been established.

Re-use of chicken litter across broiler cycles – managing the food-borne pathogen risk

Thus the objectives of this study can be broadly categorised as follows:-  Evaluate current practices adopted (e.g. litter pile-up) prior to re-use of litter for subsequent chicken cycles  To establish pathogen die-off that occurs during currently adopted methods of in-shed treatment of litter  To establish simple physical parameters to monitor this pathogen reduction and create an understanding of such reduction strategies to aid in-shed management of re-use litter  To carry out studies to assess the potential of the re-used litter (once spread) to support pathogens during a typical chicken production cycle.  To provide background data for the development of a simple code of practice for an in-shed litter pile-up process

Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia: Species differentiation and prediction for fisheries monitoring and assessment

Four species of large mackerels (Scomberomorus spp.) co-occur in the waters off northern Australia and are important to fisheries in the region. State fisheries agencies monitor these species for fisheries assessment; however, data inaccuracies may exist due to difficulties with identification of these closely related species, particularly when specimens are incomplete from fish processing. This study examined the efficacy of using otolith morphometrics to differentiate and predict among the four mackerel species off northeastern Australia. Seven otolith measurements and five shape indices were recorded from 555 mackerel specimens. Multivariate modelling including linear discriminant analysis (LDA) and support vector machines, successfully differentiated among the four species based on otolith morphometrics. Cross validation determined a predictive accuracy of at least 96% for both models. An optimum predictive model for the four mackerel species was an LDA model that included fork length, feret length, feret width, perimeter, area, roundness, form factor and rectangularity as explanatory variables. This analysis may improve the accuracy of fisheries monitoring, the estimates based on this monitoring (i.e. mortality rate) and the overall management of mackerel species in Australia.

"Description of blue tongue and other arboviruses in Northern Australia"

A serological survey of cattle from throughout Queensland and sheep from cattle/sheep interface areas was conducted to determine the distribution and prevalence of antibodies to Bluetongue virus serotypes. This information allowed preliminary designation of arbovirusfree zones and identification of livestock populations at greatest risk to introduction of exotic Bluetongue viruses. Throughout the state antibodies were detected to only serotypes I and 21. In cattle prevalence decreased with increasing distance from the coast ringing from 73% in the far north to less than I% in the southwest. In sheep, prevalence of bluetongue antibodies in the major cattle/sheep interface areas in the north-west and central Queensland ranged from O% to 5%. A system of strategically placed sentinel herds of 10 young serologically negative cattle was established across northern Australia to monitor the distribution and seasonality of bluetongue viruses. Initially 23 herds were located in Queensland, 4 in Northern Territory and 2 in Western Australia but by the completion of the project the number of herds in Queensland had been reduced to 12. No bluetongue virus activity was detected in Western Australia or Northern Territory herds throughout the project although testing of one herd in Northern Territory with a history of bluetongue activity was not done after June 1991. In Queensland, activity to bluetongue serotypes I and 21 was detected in all years of the project. Transmissions occurred predominantly in the period April to September and were more widespread in wetter years' The pathogenic bluetongue setotypes previously isolated from the Northern Territory have not spread to adjoining States.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

A network forensics tool for precise data packet capture and replay in cyber-physical systems

Network data packet capture and replay capabilities are basic requirements for forensic analysis of faults and security-related anomalies, as well as for testing and development. Cyber-physical networks, in which data packets are used to monitor and control physical devices, must operate within strict timing constraints, in order to match the hardware devices' characteristics. Standard network monitoring tools are unsuitable for such systems because they cannot guarantee to capture all data packets, may introduce their own traffic into the network, and cannot reliably reproduce the original timing of data packets. Here we present a high-speed network forensics tool specifically designed for capturing and replaying data traffic in Supervisory Control and Data Acquisition systems. Unlike general-purpose "packet capture" tools it does not affect the observed network's data traffic and guarantees that the original packet ordering is preserved. Most importantly, it allows replay of network traffic precisely matching its original timing. The tool was implemented by developing novel user interface and back-end software for a special-purpose network interface card. Experimental results show a clear improvement in data capture and replay capabilities over standard network monitoring methods and general-purpose forensics solutions.

An Investigation of Environmental Conditions Experienced During the Life of High Value Wood Components and Products

Australian forest industries have a long history of export trade of a wide range of products from woodchips(for paper manufacturing), sandalwood (essential oils, carving and incense) to high value musical instruments, flooring and outdoor furniture. For the high value group, fluctuating environmental conditions brought on by changes in mperature and relative humidity, can lead to performance problems due to consequential swelling, shrinkage and/or distortion of the wood elements. A survey determined the types of value-added products exported, including species and dimensions packaging used and export markets. Data loggers were installed with shipments to monitor temperature and relative humidity conditions. These data were converted to timber equilibrium moisture content values to provide an indication of the environment that the wood elements would be acclimatising to. The results of the initial survey indicated that primary high value wood export products included guitars, flooring, decking and outdoor furniture. The destination markets were mainly located in the northern hemisphere, particularly the United States of America, China, Hong Kong, Europe including the United Kingdom), Japan, Korea and the Middle East. Other regions importing Australian-made wooden articles were south-east Asia, New Zealand and South Africa. Different timber species have differing rates of swelling and shrinkage, so the types of timber were also recorded during the survey. Results from this work determined that the major species were ash-type eucalypts from south-eastern Australia (commonly referred to in the market as Tasmanian oak), jarrah from Western Australia, spotted gum, hoop pine, white cypress, black butt, brush box and Sydney blue gum from Queensland and New South Wales. The environmental conditions data indicated that microclimates in shipping containers can fluctuate extensively during shipping. Conditions at the time of manufacturing were usually between 10 and 12% equilibrium moisture content, however conditions during shipping could range from 5 (very dry) to 20% (very humid). The packaging systems incorporated were reported to be efficient at protecting the wooden articles from damage during transit. The research highlighted the potential risk for wood components to ‘move’ in response to periods of drier or more humid conditions than those at the time of manufacturing, and the importance of engineering a packaging system that can account for the environmental conditions experienced in shipping containers. Examples of potential dimensional changes in wooden components were calculated based on published unit shrinkage data for key species and the climatic data returned from the logging equipment. The information highlighted the importance of good design to account for possible timber movement during shipping. A timber movement calculator was developed to allow designers to input component species, dimensions, site of manufacture and destination, to see validate their product design. This calculator forms part of the free interactive website www.timbers.com.au.

Development and reproducibility of a short questionnaire to measure use and usability of custom-made orthopaedic shoes

OBJECTIVE To develop a short and easy to use questionnaire to measure use and usability of custom-made orthopaedic shoes, and to investigate its reproducibility. DESIGN Development of the questionnaire (Monitor Orthopaedic Shoes) was based on a literature search, expert interviews, 2 expert meetings, and exploration and testing of reproducibility. The questionnaire comprises 2 parts: a pre part, measuring expectations; and a post part, measuring experiences. Patients The pre part of the final version was completed twice by 37 first-time users before delivery of their orthopaedic shoes. The post part of the final version was completed twice by 39 first-time users who had worn their orthopaedic shoes for 2–4 months. RESULTS High reproducibility scores (Cohen’s kappa > 0.60 or intra class correlation > 0.70) were found in all but one question of both parts of the final version of the Monitor Orthopaedic Shoes questionnaire. The smallest real difference on a visual analogue scale (100 mm) ranged from 21 to 50 mm. It took patients approximately 15 minutes to complete one part. CONCLUSION Monitor Orthopaedic Shoes is a practical and reproducible questionnaire that can measure relevant aspects of use and usability of orthopaedic shoes from a patient’s perspective.

IWGDF guidance on the prevention of foot ulcers in at-risk patients with diabetes

Recommendations - 1 To identify a person with diabetes at risk for foot ulceration, examine the feet annually to seek evidence for signs or symptoms of peripheral neuropathy and peripheral artery disease. (GRADE strength of recommendation: strong; Quality of evidence: low) - 2 In a person with diabetes who has peripheral neuropathy, screen for a history of foot ulceration or lower-extremity amputation, peripheral artery disease, foot deformity, pre-ulcerative signs on the foot, poor foot hygiene and ill-fitting or inadequate footwear. (Strong; Low) - 3 Treat any pre-ulcerative sign on the foot of a patient with diabetes. This includes removing callus, protecting blisters and draining when necessary, treating ingrown or thickened toe nails, treating haemorrhage when necessary and prescribing antifungal treatment for fungal infections. (Strong; Low) - 4 To protect their feet, instruct an at-risk patient with diabetes not to walk barefoot, in socks only, or in thin-soled standard slippers, whether at home or when outside. (Strong; Low) - 5 Instruct an at-risk patient with diabetes to daily inspect their feet and the inside of their shoes, daily wash their feet (with careful drying particularly between the toes), avoid using chemical agents or plasters to remove callus or corns, use emollients to lubricate dry skin and cut toe nails straight across. (Weak; Low) - 6 Instruct an at-risk patient with diabetes to wear properly fitting footwear to prevent a first foot ulcer, either plantar or non-plantar, or a recurrent non-plantar foot ulcer. When a foot deformity or a pre-ulcerative sign is present, consider prescribing therapeutic shoes, custom-made insoles or toe orthosis. (Strong; Low) - 7 To prevent a recurrent plantar foot ulcer in an at-risk patient with diabetes, prescribe therapeutic footwear that has a demonstrated plantar pressure-relieving effect during walking (i.e. 30% relief compared with plantar pressure in standard of care therapeutic footwear) and encourage the patient to wear this footwear. (Strong; Moderate) - 8 To prevent a first foot ulcer in an at-risk patient with diabetes, provide education aimed at improving foot care knowledge and behaviour, as well as encouraging the patient to adhere to this foot care advice. (Weak; Low) - 9 To prevent a recurrent foot ulcer in an at-risk patient with diabetes, provide integrated foot care, which includes professional foot treatment, adequate footwear and education. This should be repeated or re-evaluated once every 1 to 3 months as necessary. (Strong; Low) - 10 Instruct a high-risk patient with diabetes to monitor foot skin temperature at home to prevent a first or recurrent plantar foot ulcer. This aims at identifying the early signs of inflammation, followed by action taken by the patient and care provider to resolve the cause of inflammation. (Weak; Moderate) - 11 Consider digital flexor tenotomy to prevent a toe ulcer when conservative treatment fails in a high-risk patient with diabetes, hammertoes and either a pre-ulcerative sign or an ulcer on the distal toe. (Weak; Low) - 12 Consider Achilles tendon lengthening, joint arthroplasty, single or pan metatarsal head resection, or osteotomy to prevent a recurrent foot ulcer when conservative treatment fails in a high-risk patient with diabetes and a plantar forefoot ulcer. (Weak; Low) - 13 Do not use a nerve decompression procedure in an effort to prevent a foot ulcer in an at-risk patient with diabetes, in preference to accepted standards of good quality care. (Weak; Low)