Regulation of dormancy in barley by blue light and after-ripening : effects on abscisic acid and gibberellin metabolism

White light strongly promotes dormancy in freshly harvested cereal grains, whereas dark and after-ripening have the opposite effect. We have analyzed the interaction of light and after-ripening on abscisic acid (ABA) and gibberellin (GA) metabolism genes and dormancy in barley (Hordeum vulgare ‘Betzes’). Analysis of gene expression in imbibed barley grains shows that different ABA metabolism genes are targeted by white light and after-ripening. Of the genes examined, white light promotes the expression of an ABA biosynthetic gene, HvNCED1, in embryos. Consistent with this result, enzyme-linked immunosorbent assays show that dormant grains imbibed under white light have higher embryo ABA content than grains imbibed in the dark. After-ripening has no effect on expression of ABA biosynthesis genes, but promotes expression of an ABA catabolism gene (HvABA8′OH1), a GA biosynthetic gene (HvGA3ox2), and a GA catabolic gene (HvGA2ox3) following imbibition. Blue light mimics the effects of white light on germination, ABA levels, and expression of GA and ABA metabolism genes. Red and far-red light have no effect on germination, ABA levels, or HvNCED1. RNA interference experiments in transgenic barley plants support a role of HvABA8′OH1 in dormancy release. Reduced HvABA8′OH1 expression in transgenic HvABA8′OH1 RNAi grains results in higher levels of ABA and increased dormancy compared to nontransgenic grains.

Carrot mottle mimic virus (CMoMV): A second umbravirus associated with carrot motley dwarf disease recognised by nucleic acid hybridisation

Carrot mottle umbravirus (CMoV) has always been found co-infecting plants with carrot red leaf luteovirus (CRLV) and in carrot (Daucus carota) these co-infections are associated with carrot motley dwarf disease (CMD). CMD occurs wherever carrots are grown. Hence, CMoV was believed to have a corresponding global distribution. However, little or no hybridisation was detected between cDNA generated from the sequenced Australian isolate of CMoV (CMoV-A) and RNA from the much studied Scottish isolate of CMoV (CMoV-S). A weak hybridisation signal was obtained using cDNA to a conserved part of the RNA-dependent RNA polymerase gene of CMoV-A, but when cDNAs to other parts of the CMoV-A genome were used as probes there was no detectable hybridisation with CMoV-S RNA. This lack of hybridisation suggests that the two virus isolates have relatively divergent genomes and that they should be regarded as distinct virus species. Both viruses are transmitted by Cavariella aegopodii, but only with the help of CRLV, and they yield almost identical double-stranded RNA profiles. For these reasons, we propose that the CMoV isolate from Australia be renamed carrot mottle mimic umbravirus (CMoMV). cDNA to CMoMV RNA hybridised with RNA from an isolate from New Zealand, whereas cDNA to CMoV-S RNA hybridised with RNA from isolates from England and Morocco but not to RNA from the isolate from New Zealand. Although preliminary, these data suggest that CMoV and CMoMV may have different global distributions.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection

Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularly for small to medium aircraft such as unmanned aerial vehicles). In this paper, using relative entropy rate concepts, we propose and investigate a new change detection approach that uses hidden Markov model filters to sequentially detect aircraft manoeuvres from morphologically processed image sequences. Experiments using simulated and airborne image sequences illustrate the performance of our proposed algorithm in comparison to other sequential change detection approaches applied to this application.

Practical Modbus flooding attack and detection

The Modicon Communication Bus (Modbus) protocol is one of the most commonly used protocols in industrial control systems. Modbus was not designed to provide security. This paper confirms that the Modbus protocol is vulnerable to flooding attacks. These attacks involve injection of commands that result in disrupting the normal operation of the control system. This paper describes a set of experiments that shows that an anomaly-based change detection algorithm and signature-based Snort threshold module are capable of detecting Modbus flooding attacks. In comparing these intrusion detection techniques, we find that the signature-based detection requires a carefully selected threshold value, and that the anomaly-based change detection algorithm may have a short delay before detecting the attacks depending on the parameters used. In addition, we also generate a network traffic dataset of flooding attacks on the Modbus control system protocol.

Uric acid and xanthine oxidoreductase in wound healing

Chronic wounds are an important health problem because they are difficult to heal and treatment is often complicated, lengthy and expensive. For a majority of sufferers the most common outcomes are long-term immobility, infection and prolonged hospitalisation. There is therefore an urgent need for effective therapeutics that will enhance ulcer healing and patient quality of life, and will reduce healthcare costs. Studies in our laboratory have revealed elevated levels of purine catabolites in wound fluid from patients with venous leg ulcers. In particular, we have discovered that uric acid is elevated in wound fluid, with higher concentrations correlating with increased wound severity. We have also revealed a corresponding depletion in uric acid precursors, including adenosine. Further, we have revealed that xanthine oxidoreductase, the enzyme that catalyses the production of uric acid, is present at elevated levels in wound fluid. Taken together, these findings provide evidence that xanthine oxidoreductase may have a function in the formation or persistence of chronic wounds. Here we describe the potential function of xanthine oxidoreductase and uric acid accumulation in the wound site, and the effect of xanthine oxidoreductase in potentiating the inflammatory response.

The highly abundant urinary metabolite urobilin interferes with the bicinchoninic acid assay

Estimation of total protein concentration is an essential step in any protein- or peptide-centric analysis pipeline. This study demonstrates that urobilin, a breakdown product of heme and a major constituent of urine, interferes considerably with the bicinchoninic acid (BCA) assay. This interference is probably due to the propensity of urobilin to reduce cupric ions (Cu2+) to cuprous ions (Cu1+), thus mimicking the reduction of copper by proteins, which the assay was designed to do. In addition, it is demonstrated that the Bradford assay is more resistant to the influence of urobilin and other small molecules. As such, urobilin has a strong confounding effect on the estimate of total protein concentrations obtained by BCA assay and thus this assay should not be used for urinary protein quantification. It is recommended that the Bradford assay be used instead.

Hydraulic instability onset detection in Kaplan turbines by monitoring shaft vibrations

Design of hydraulic turbines has often to deal with hydraulic instability. It is well-known that Francis and Kaplan types present hydraulic instability in their design power range. Even if modern CFD tools may help to define these dangerous operating conditions and optimize runner design, hydraulic instabilities may fortuitously arise during the turbine life and should be timely detected in order to assure a long-lasting operating life. In a previous paper, the authors have considered the phenomenon of helical vortex rope, which happens at low flow rates when a swirling flow, in the draft tube conical inlet, occupies a large portion of the inlet. In this condition, a strong helical vortex rope appears. The vortex rope causes mechanical effects on the runner, on the whole turbine and on the draft tube, which may eventually produce severe damages on the turbine unit and whose most evident symptoms are vibrations. The authors have already shown that vibration analysis is suitable for detecting vortex rope onset, thanks to an experimental test campaign performed during the commissioning of a 23 MW Kaplan hydraulic turbine unit. In this paper, the authors propose a sophisticated data driven approach to detect vortex rope onset at different power load, based on the analysis of the vibration signals in the order domain and introducing the so-called "residual order spectrogram", i.e. an order-rotation representation of the vibration signal. Some experimental test runs are presented and the possibility to detect instability onset, especially in real-time, is discussed.

Brisbane Macroscopic Fundamental Diagram : empirical findings on network partitioning and incident detection

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow, and the existence with dynamic features was confirmed in congested urban network in downtown Yokohama with real data set. Since the MFD represents the area-wide network traffic performances, studies on perimeter control strategies and an area traffic state estimation utilizing the MFD concept has been reported. However, limited works have been reported on real world example from signalised arterial network. This paper fuses data from multiple sources (Bluetooth, Loops and Signals) and develops a framework for the development of the MFD for Brisbane, Australia. Existence of the MFD in Brisbane arterial network is confirmed. Different MFDs (from whole network and several sub regions) are evaluated to discover the spatial partitioning in network performance representation. The findings confirmed the usefulness of appropriate network partitioning for traffic monitoring and incident detections. The discussion addressed future research directions

Modal flexibility method for structural damage detection in suspension bridges

Suspension bridges meet the steadily growing demand for lighter and longer bridges in today’s infrastructure systems. These bridges are designed to have long life spans, but with age, their main cables and hangers could suffer from corrosion and fatigue. There is a need for a simple and reliable procedure to detect and locate such damage, so that appropriate retrofitting can be carried out to prevent bridge failure. Damage in a structure causes changes in its properties (mass, damping and stiffness) which in turn will cause changes in its vibration characteristics (natural frequencies, modal damping and mode shapes). Methods based on modal flexibility, which depends on both the natural frequencies and mode shapes, have the potential for damage detection. They have been applied successfully to beam and plate elements, trusses and simple structures in reinforced concrete and steel. However very limited applications for damage detection in suspension bridges have been identified to date. This paper examines the potential of modal flexibility methods for damage detection and localization of a suspension bridge under different damage scenarios in the main cables and hangers using numerical simulation techniques. Validated finite element model (FEM) of a suspension bridge is used to acquire mass normalized mode shape vectors and natural frequencies at intact and damaged states. Damage scenarios will be simulated in the validated FE models by varying stiffness of the damaged structural members. The capability of damage index based on modal flexibility to detect and locate damage is evaluated. Results confirm that modal flexibility based methods have the ability to successfully identify damage in suspension bridge main cables and hangers.

Targeting amino acid transport in metastatic castration-resistant prostate cancer : effects on cell cycle, cell growth, and tumor development

Background L-type amino acid transporters (LATs) uptake neutral amino acids including L-leucine into cells, stimulating mammalian target of rapamycin complex 1 signaling and protein synthesis. LAT1 and LAT3 are overexpressed at different stages of prostate cancer, and they are responsible for increasing nutrients and stimulating cell growth. Methods We examined LAT3 protein expression in human prostate cancer tissue microarrays. LAT function was inhibited using a leucine analog (BCH) in androgen-dependent and -independent environments, with gene expression analyzed by microarray. A PC-3 xenograft mouse model was used to study the effects of inhibiting LAT1 and LAT3 expression. Results were analyzed with the Mann-Whitney U or Fisher exact tests. All statistical tests were two-sided. Results LAT3 protein was expressed at all stages of prostate cancer, with a statistically significant decrease in expression after 4–7 months of neoadjuvant hormone therapy (4–7 month mean = 1.571; 95% confidence interval = 1.155 to 1.987 vs 0 month = 2.098; 95% confidence interval = 1.962 to 2.235; P = .0187). Inhibition of LAT function led to activating transcription factor 4–mediated upregulation of amino acid transporters including ASCT1, ASCT2, and 4F2hc, all of which were also regulated via the androgen receptor. LAT inhibition suppressed M-phase cell cycle genes regulated by E2F family transcription factors including critical castration-resistant prostate cancer regulatory genes UBE2C, CDC20, and CDK1. In silico analysis of BCH-downregulated genes showed that 90.9% are statistically significantly upregulated in metastatic castration-resistant prostate cancer. Finally, LAT1 or LAT3 knockdown in xenografts inhibited tumor growth, cell cycle progression, and spontaneous metastasis in vivo. Conclusion Inhibition of LAT transporters may provide a novel therapeutic target in metastatic castration-resistant prostate cancer, via suppression of mammalian target of rapamycin complex 1 activity and M-phase cell cycle genes.

Damage detection in cable structures using vibration characteristics

Cable structures find many applications such as in power transmission, in anchors and especially in bridges. They serve as major load bearing elements in suspension bridges, which are capable of spanning long distances. All bridges, including suspension bridges, are designed to have long service lives. However, during this long life, they become vulnerable to damage due to changes in loadings, deterioration with age and random action such as impacts. The main cables are more vulnerable to corrosion and fatigue, compared to the other bridge components, and consequently reduces the serviceability and ultimate capacity of the bridge. Detecting and locating such damage at the earliest stage is challenging in the current structural health monitoring (SHM) systems of long span suspension bridges. Damage or deterioration of a structure alters its stiffness, mass and damping properties which in turn modify its vibration characteristics. This phenomenon can therefore be used to detect damage in a structure. The modal flexibility, which depends on the vibration characteristics of a structure, has been identified as a successful damage indicator in beam and plate elements, trusses and simple structures in reinforced concrete and steel. Successful application of the modal flexibility phenomenon to detect and locate the damage in suspension bridge main cables has received limited attention in recent research work. This paper, therefore examines the potential of the modal flexibility based Damage Index (DI) for detecting and locating damage in the main cable of a suspension bridge under four different damage scenarios. Towards this end, a numerical model of a suspension bridge cable was developed to extract the modal parameters at both damaged and undamaged states. Damage scenarios considered in this study with varied location and severity were simulated by changing stiffness at particular locations of the cable model. Results confirm that the DI has the potential to successfully detect and locate damage in suspension bridge main cables. This simple method can therefore enable bridge engineers and managers to detect and locate damage in suspension bridges at an early stage, minimize expensive retrofitting and prevent bridge collapse.

Social event detection at MediaEval 2013 : challenges, datasets and evaluation

In this paper, we provide an overview of the Social Event Detection (SED) task that is part of the MediaEval Bench mark for Multimedia Evaluation 2013. This task requires participants to discover social events and organize the re- lated media items in event-specific clusters within a collection of Web multimedia. Social events are events that are planned by people, attended by people and for which the social multimedia are also captured by people. We describe the challenges, datasets, and the evaluation methodology.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.