Scalable distributed event detection for Twitter

Social media streams, such as Twitter, have shown themselves to be useful sources of real-time information about what is happening in the world. Automatic detection and tracking of events identified in these streams have a variety of real-world applications, e.g. identifying and automatically reporting road accidents for emergency services. However, to be useful, events need to be identified within the stream with a very low latency. This is challenging due to the high volume of posts within these social streams. In this paper, we propose a novel event detection approach that can both effectively detect events within social streams like Twitter and can scale to thousands of posts every second. Through experimentation on a large Twitter dataset, we show that our approach can process the equivalent to the full Twitter Firehose stream, while maintaining event detection accuracy and outperforming an alternative distributed event detection system

Can twitter replace newswire for breaking news?

Twitter is often considered to be a useful source of real-time news, potentially replacing newswire for this purpose. But is this true? In this paper, we examine the extent to which news reporting in newswire and Twitter overlap and whether Twitter often reports news faster than traditional newswire providers. In particular, we analyse 77 days worth of tweet and newswire articles with respect to both manually identified major news events and larger volumes of automatically identified news events. Our results indicate that Twitter reports the same events as newswire providers, in addition to a long tail of minor events ignored by mainstream media. However, contrary to popular belief, neither stream leads the other when dealing with major news events, indicating that the value that Twitter can bring in a news setting comes predominantly from increased event coverage, not timeliness of reporting

Understanding the role of microRNA expression in the response to phenobarbital toxicity in the rat

New chemical entities that humans and animals are likely to be exposed to are tested using various in vitro and in vivo toxicological assays to understand the potential for harm. The increasing power of in silico analysis in combination with in vitro techniques is being exploited for the ability to predict in vivo outcomes such as toxicity, thereby reducing the requirement for animal testing. Previous studies in our laboratory have found that dietary phenobarbital treatment of rats results in dysregulation of the hepatic miRNAome, including prominent alteration of the expression of the miR-200a/b/429 cluster and miR-182/96 cluster. In this thesis I explore the use of a cell culture system, rat hepatocyte-like B13/H cells, to interrogate the underlying mechanisms of phenobarbital-mediated toxicity. Through this model I explore the function of phenobarbital-dysregulated microRNAs to determine their roles as markers of phenobarbital treatment in the rat. We find that the B13/H cells provide a useful system for studying the phenobarbital response in vitro, and how these responses relate to the effects of oral exposure to phenobarbital in vivo in the rat. In this respect, the B13/H cells appear representative of the rat response to phenobarbital, which is different to that of the mouse. With a focus on the phenobarbital-mediated dysregulation of the miR-182/96 cluster, I have explored the consequences of phenobarbital treatment on the B13/H cell model and related this to the in vivo effects. The data suggest that these microRNAs appear to control phenobarbital-mediated perturbation of glycolysis, cell transformation and methylation patterns.Open Acces

Investigating osteoarthritis in the human hip using three-dimensional finite element models

This is the author accepted manuscript. The final version is available from the International Society of Biomechanics via the link in this recor

Nitrogen addition alters composition, diversity, and functioning of microbial communities in mangrove soils : an incubation experiment

Mangrove ecosystems are important for carbon storage due to their high productivity and low decomposition rates. Waterways have experienced increased nutrient loads as a result of anthropogenic activities and it is unclear how this may affect carbon and nutrient cycles in downstream mangroves that receive these nutrient-rich waters. Using a laboratory-based incubation experiment, this study aimed to assess the effects of nutrient addition on the diversity and structure of mangrove soil bacterial communities, as well as biomass and activity of the soil microbial community, under different oxygen conditions. Bacterial community diversity and composition was characterised using 16S rRNA gene sequencing and microbial activity was examined through the measurement of microbial respiration and the activities of enzymes associated with organic matter decomposition. Nitrogen addition caused clear shifts in bacterial community composition, with decreases in bacterial diversity and the abundance of sulfate-reducing bacteria. Microbial biomass also decreased with nitrogen addition under reduced oxygen incubations. Changes in bacterial community structure were accompanied by changes in the activity of some enzymes involved in carbon, nitrogen and phosphorus cycling. Under reduced oxygen conditions, nitrogen addition resulted in a significant increase in the microbial metabolic quotient but no accompanying change in microbial respiration, which was explained by a decrease in microbial biomass. The findings of this study indicate that nitrogen loading has potential implications for microbial communities and carbon and nutrient cycling in mangrove environments that warrant further investigation under field conditions

The Impact of Experimental Pain on Shoulder Movement During an Arm Elevated Reaching Task in a Virtual Reality Environment

Background: People with chronic shoulder pain have been shown to present with motor adaptations during arm movements. These adaptations may create abnormal physical stress on shoulder tendons and muscles. However, how and why these adaptations develop from the acute stage of pain is still not well-understood. Objective: To investigate motor adaptations following acute experimental shoulder pain during upper limb reaching. Methods: Forty participants were assigned to the Control or Pain group. They completed a task consisting of reaching targets in a virtual reality environment at three time points: (1) baseline (both groups pain-free), (2) experimental phase (Pain group experiencing acute shoulder pain induced by injecting hypertonic saline into subacromial space), and (3) Post experimental phase (both groups pain-free). Electromyographic (EMG) activity, kinematics, and performance data were collected. Results: The Pain group showed altered movement planning and execution as shown by a significant increased delay to reach muscles EMG peak and a loss of accuracy, compared to controls that have decreased their mean delay to reach muscles peak and improved their movement speed through the phases. The Pain group also showed protective kinematic adaptations using less shoulder elevation and elbow flexion, which persisted when they no longer felt the experimental pain. Conclusion: Acute experimental pain altered movement planning and execution, which affected task performance. Kinematic data also suggest that such adaptations may persist over time, which could explain those observed in chronic pain populations

Bridging the reality gap in quantum devices with physics-aware machine learning

The discrepancies between reality and simulation impede the optimization and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of material defects is one of the major contributions to the reality gap. We bridge this gap using physics-aware machine learning, in particular, using an approach combining a physical model, deep learning, Gaussian random field, and Bayesian inference. This approach enables us to infer the disorder potential of a nanoscale electronic device from electron-transport data. This inference is validated by verifying the algorithm’s predictions about the gate-voltage values required for a laterally defined quantum-dot device in AlGaAs/GaAs to produce current features corresponding to a double-quantum-dot regime