Enrichment of raw sensor data to enable high-level queries

Sensor networks are increasingly used across various application domains. Their usage has the advantage of automated, often continuous, monitoring of activities and events. Ubiquitous sensor networks detect location of people and objects and their movement. In our research, we employ a ubiquitous sensor network to track the movement of players in a tennis match. By doing so, our goal is to create a detailed analysis of how the match progressed, recording points scored, games and sets, and in doing so, greatly reduce the eort of coaches and players who are required to study matches afterwards. The sensor network is highly efficient as it eliminates the need for manual recording of the match. However, it generates raw data that is unusable by domain experts as it contains no frame of reference or context and cannot be analyzed or queried. In this work, we present the UbiQuSE system of data transformers which bridges the gap between raw sensor data and the high-level requirements of domain specialists such as the tennis coach

Expanding sensor networks to automate knowledge acquisition

The availability of accurate, low-cost sensors to scientists has resulted in widespread deployment in a variety of sporting and health environments. The sensor data output is often in a raw, proprietary or unstructured format. As a result, it is often difficult to query multiple sensors for complex properties or actions. In our research, we deploy a heterogeneous sensor network to detect the various biological and physiological properties in athletes during training activities. The goal for exercise physiologists is to quickly identify key intervals in exercise such as moments of stress or fatigue. This is not currently possible because of low level sensors and a lack of query language support. Thus, our motivation is to expand the sensor network with a contextual layer that enriches raw sensor data, so that it can be exploited by a high level query language. To achieve this, the domain expert specifies events in a tradiational event-condition-action format to deliver the required contextual enrichment

Extracting tennis statistics from wireless sensing environments

Creating statistics from sporting events is now widespread with most eorts to automate this process using various sensor devices. The problem with many of these statistical applications is that they require proprietary applications to process the sensed data and there is rarely an option to express a wide range of query types. Instead, applications tend to contain built-in queries with predened outputs. In the research presented in this paper, data from a wireless network is converted to a structured and highly interoperable format to facilitate user queries by expressing high level queries in a standard database language and automatically generating the results required by coaches

Querying XML data streams from wireless sensor networks: an evaluation of query engines

As the deployment of wireless sensor networks increase and their application domain widens, the opportunity for effective use of XML filtering and streaming query engines is ever more present. XML filtering engines aim to provide efficient real-time querying of streaming XML encoded data. This paper provides a detailed analysis of several such engines, focusing on the technology involved, their capabilities, their support for XPath and their performance. Our experimental evaluation identifies which filtering engine is best suited to process a given query based on its properties. Such metrics are important in establishing the best approach to filtering XML streams on-the-fly

Characterization of wild and captive baboon gut microbiota and their antibiotic resistomes

Antibiotic exposure results in acute and persistent shifts in the composition and function of microbial communities associated with vertebrate hosts. However, little is known about the state of these communities in the era before the widespread introduction of antibiotics into clinical and agricultural practice. We characterized the fecal microbiota and antibiotic resistomes of wild and captive baboon populations to understand the effect of human exposure and to understand how the primate microbiota may have been altered during the antibiotic era. We used culture-independent and bioinformatics methods to identify functional resistance genes in the guts of wild and captive baboons and show that exposure to humans is associated with changes in microbiota composition and resistome expansion compared to wild baboon groups. Our results suggest that captivity and lifestyle changes associated with human contact can lead to marked changes in the ecology of primate gut communities.Environmental microbes have harbored the capacity for antibiotic production for millions of years, spanning the evolution of humans and other vertebrates. However, the industrial-scale use of antibiotics in clinical and agricultural practice over the past century has led to a substantial increase in exposure of these agents to human and environmental microbiota. This perturbation is predicted to alter the ecology of microbial communities and to promote the evolution and transfer of antibiotic resistance (AR) genes. We studied wild and captive baboon populations to understand the effects of exposure to humans and human activities (e.g., antibiotic therapy) on the composition of the primate fecal microbiota and the antibiotic-resistant genes that it collectively harbors (the “resistome”). Using a culture-independent metagenomic approach, we identified functional antibiotic resistance genes in the gut microbiota of wild and captive baboon groups and saw marked variation in microbiota architecture and resistomes across habitats and lifeways. Our results support the view that antibiotic resistance is an ancient feature of gut microbial communities and that sharing habitats with humans may have important effects on the structure and function of the primate microbiota

The electrochemical deposition of polyaniline at pure aluminium: electrochemical activity and corrosion protection properties

Polyaniline films were electrodeposited at pure aluminium from a tosylic acid solution containing aniline. These polymer films exhibited similar characteristics as pure polyaniline electrosynthesized at an inert platinum electrode, when removed from their respective substrates and dissolved in NMP. Both polymers had similar molecular weights and similar UV-visible absorption spectra. However, the aluminium substrate had a considerable effect on the electrochemical activity of the films. The polyaniline films deposited at aluminium appeared to lose electroactivity and the electrochemical impedance data were governed by the oxidized aluminium substrate. This is consistent with a galvanic interaction between the polymer and the aluminium substrate, giving rise to oxidation of the aluminium and reduction of the polymer. The polyaniline deposits appeared to offer only a slight increase in the corrosion resistance of aluminium. Surface potential measurements, using a scanning vibrating probe, showed that attack initiated underneath the polymer under anodic polarization conditions, indicating that chloride anions diffuse across the polymer to react at the underlying aluminium substrate

A framework for user-assisted knowledge acquisition from sensor data

The availability of a new generation of accurate, low-cost sensors to scientists has resulted in widespread deployment of these sensors in a variety of environments. Data generated by these devices are often in a raw, proprietary or unstructured format. As a result, it is difficult for scientists to analyse or query across various biological and physiological sensor data values. There exists both a physical-digital divide between sensor data with related real-world conditions, and a knowledge divide between the information needs of domain specialists. A key challenge is to bridge these divisions in order to allow scientists to make better decisions based on the sensed in- formation. The goal of this research is to show that low level data collection resources such as sensors can be used for high level query expressions and knowledge extraction, without the need for expensive human based operations. To achieve this goal, it was necessary to deliver a generic approach to enriching raw sensor data, providing information services to enable the end user to acquire knowledge from low-level sensor data and defining an integration framework for sensor data and related contextual information. As a result, key research questions of interpreting heterogeneous sensor data, enriching sensor data with contextual information, and integrating sensor data with related participant and environmental information, are tackled over the course of this dissertation

Systematic review and individual patient data meta-analysis of sex differences in depression and prognosis in persons with myocardial infarction: a MINDMAPS study

Objective - Using combined individual patient data (IPD) from prospective studies, we explored sex differences in depression and prognosis post-myocardial infarction (MI), and determined whether disease indices could account for found differences. Methods - Meta-analysis of IPD from 10,175 MI patients who completed diagnostic interviews or depression questionnaires from 16 prospective studies of MI patients, identified by systematic review for the MINDMAPS study. Multilevel logistic and Cox regression models were used to determine sex differences in prevalence of depression and sex-specific effects of depression on subsequent cardiovascular morbidity and all-cause mortality. Results - Combined interview and questionnaire data from observational studies showed that 36% (635/1760) of women and 29% (1575/5526) of men reported elevated levels of depression (age-adjusted OR=0.68, 95% CI 0.60 to 0.77, p (sex*depression interaction p Conclusions - The prevalence of depression post-MI was higher in women than men, but the association between depression and cardiac prognosis was worse for men. LVEF was associated with depression in men only, and accounted for the increased risk of all-cause mortality in depressed men versus women, suggesting that depression in men post-MI may in part reflect cardiovascular disease severity

Fine-Scale Mapping of the 4q24 Locus Identifies Two Independent Loci Associated with Breast Cancer Risk

Background: A recent association study identified a common variant (rs9790517) at 4q24 to be associated with breast cancer risk. Independent association signals and potential functional variants in this locus have not been explored. Methods: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium. Results: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10−4; OR, 1.04; 95% confidence interval (CI), 1.02–1.07] and rs77928427 (P = 1.86 × 10−4; OR, 1.04; 95% CI, 1.02–1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r2 ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor–binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue. Conclusion: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2. Impact: Fine-mapping study with large sample size warranted for identification of independent loci for breast cancer risk