Tracking the Fate of Microbially Sequestered Carbon Dioxide in Soil Organic Matter

The microbial contribution to soil organic matter (SOM) has recently been shown to be much larger than previously thought and thus its role in carbon sequestration may also be underestimated. In this study we employ C-13 ((CO2)-C-13) to assess the potential CO2 sequestration capacity of soil chemoautotrophic bacteria and combine nuclear magnetic resonance (NMR) with stable isotope probing (SIP), techniques that independently make use of the isotopic enrichment of soil microbial biomass. In this way molecular information generated from NMR is linked with identification of microbes responsible for carbon capture. A mathematical model is developed to determine real-time CO2 flux so that net sequestration can be calculated. Twenty-eight groups of bacteria showing close homologies with existing species were identified. Surprisingly, Ralstonia eutropha was the dominant group. Through NMR we observed the formation of lipids, carbohydrates, and proteins produced directly from CO2 utilized by microbial biomass. The component of SOM directly associated with CO2 capture was calculated at 2.86 mg C (89.21 mg kg(-1)) after 48 h. This approach can,differentiate between SOM derived through microbial uptake of CO2 and other SOM constituents and represents a first step in tracking the fate and dynamics of microbial biomass in soil.

Behavioural context of multi-scale species distribution models assessed by radio-tracking

Incorporating ecological processes and animal behaviour into Species Distribution Models (SDMs) is difficult. In species with a central resting or breeding place, there can be conflict between the environmental requirements of the 'central place' and foraging habitat. We apply a multi-scale SDM to examine habitat trade-offs between the central place, roost sites, and foraging habitat in . Myotis nattereri. We validate these derived associations using habitat selection from behavioural observations of radio-tracked bats. A Generalised Linear Model (GLM) of roost occurrence using land cover variables with mixed spatial scales indicated roost occurrence was positively associated with woodland on a fine scale and pasture on a broad scale. Habitat selection of radio-tracked bats mirrored the SDM with bats selecting for woodland in the immediate vicinity of individual roosts but avoiding this habitat in foraging areas, whilst pasture was significantly positively selected for in foraging areas. Using habitat selection derived from radio-tracking enables a multi-scale SDM to be interpreted in a behavioural context. We suggest that the multi-scale SDM of . M. nattereri describes a trade-off between the central place and foraging habitat. Multi-scale methods provide a greater understanding of the ecological processes which determine where species occur and allow integration of behavioural processes into SDMs. The findings have implications when assessing the resource use of a species at a single point in time. Doing so could lead to misinterpretation of habitat requirements as these can change within a short time period depending on specific behaviour, particularly if detectability changes depending on behaviour. © 2011 Gesellschaft für ökologie.

Analysis of Dependence Tracking Algorithms for Task Dataflow Execution

Processor architectures has taken a turn towards many-core processors, which integrate multiple processing cores on a single chip to increase overall performance, and there are no signs that this trend will stop in the near future. Many-core processors are harder to program than multi-core and single-core processors due to the need of writing parallel or concurrent programs with high degrees of parallelism. Moreover, many-cores have to operate in a mode of strong scaling because of memory bandwidth constraints. In strong scaling increasingly finer-grain parallelism must be extracted in order to keep all processing cores busy.

Task dataflow programming models have a high potential to simplify parallel program- ming because they alleviate the programmer from identifying precisely all inter-task de- pendences when writing programs. Instead, the task dataflow runtime system detects and enforces inter-task dependences during execution based on the description of memory each task accesses. The runtime constructs a task dataflow graph that captures all tasks and their dependences. Tasks are scheduled to execute in parallel taking into account dependences specified in the task graph.

Several papers report important overheads for task dataflow systems, which severely limits the scalability and usability of such systems. In this paper we study efficient schemes to manage task graphs and analyze their scalability. We assume a programming model that supports input, output and in/out annotations on task arguments, as well as commutative in/out and reductions. We analyze the structure of task graphs and identify versions and generations as key concepts for efficient management of task graphs. Then, we present three schemes to manage task graphs building on graph representations, hypergraphs and lists. We also consider a fourth edge-less scheme that synchronizes tasks using integers. Analysis using micro-benchmarks shows that the graph representation is not always scalable and that the edge-less scheme introduces least overhead in nearly all situations.

Tracking down the cause of proteinuria in primary care

Proteinuria originates from the kidney and occurs as a result of injury to either the glomerulus or the renal tubule or both. It is relatively common in the general population with reported point prevalence of up to 8% but the prevalence falls to around 2% on repeated testing. Chronic glomerular injury resulting in proteinuria may be secondary to prolonged duration of diabetes or hypertension. A tubular origin of proteinuria may be associated with inflammation of renal tubules triggered by prescribed drugs or ingested toxins. In the absence of obvious clues to the cause of persistent proteinuria on history or clinical examination it is worthwhile reviewing the patient's prescribed drugs to identify any potentially nephrotoxic agents e.g. NSAIDs. NICE guidelines recommend screening for proteinuria in individuals at higher risk for chronic kidney disease (CKD). These include patients with diabetes, hypertension, cardiovascular disease, connective tissue disorders, a family history of renal disease and those prescribed potentially nephrotoxic drugs. Patients with sudden onset of lower limb oedema and associated proteinuria should have a serum albumin level measured to exclude the nephrotic syndrome. Renal tract ultrasound will measure kidney size, and detect scarring associated with chronic pyelonephritis or prior renal stone disease which can cause proteinuria.

Identification and output tracking control of Hammerstein systems through wireless networks

his paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.

The use of eye-tracking to explore social difficulties in cognitively able students with autism spectrum disorder: A pilot study

Individuals with autism spectrum disorder do not just 'grow out of' their early difficulties in understanding the social world. Even for those who are cognitively able, autism-related difficulties continue into adulthood. Atypicalities attending to and interpreting communicative signals from others can provide barriers to success in education, employment and relationships. In the current study, we use eye-tracking during real social interaction to explore attention to social cues (e.g. face, eyes, mouth) and links to social awareness in a group of cognitively able University students with autism spectrum disorder and typically developing students from the same University. During the interaction, students with autism spectrum disorder showed less eye fixation and more mouth fixation than typically developing students. Importantly, while 63% of typically developing participants reported thinking they were deceived about the true nature of the interaction, only 9% of autism spectrum disorder participants picked up this subtle social signal. We argue that understanding how these social attentional and social awareness difficulties manifest during adulthood is important given the growing number of adults with autism spectrum disorder who are attending higher level education. These adults may be particularly susceptible to drop-out due to demands of coping in situations where social awareness is so important.

A Novel Semi-Automated Method of Tracking Fetal Movements

We have designed software that can â€â€™look’’ at recorded ultrasound sequences. We analyzed fifteen video sequences representing recorded ultrasound scans of nine fetuses. Our method requires a small amount of user labelled pixels for processing the first frame. These initialize GrowCut 1 , a background removal algorithm, which was used for separating the fetus from its surrounding environment (segmentation). For each subsequent frame, user input is no longer necessary as some of the pixels will inherit labels from the previously processed frame. This results in our software’s ability to track movement. Two sonographers rated the results of our computer’s â€vision’ on a scale from 1 (poor fit) to 10 (excellent fit). They assessed tracking accuracy for the entire video as well as segmentation accuracy (the ability to identify fetus from non-fetus) for every 100th processed frame. There was no appreciable deterioration in the software’s ability to track the fetus over time. I

Feasibility of Undertaking Off-Site Infant Eye-Tracking Assessments of Neuro-Cognitive Functioning in Early-Intervention Centres

Recent work suggests that differences in functional brain development are already identifiable in 6- to 9-month-old infants from low socio-economic status (SES) backgrounds. Investigation of early SES-related differences in neuro-cognitive functioning requires the recruitment of large and diverse samples of infants, yet it is often difficult to persuade low-SES parents to come to a university setting. One solution is to recruit infants through early intervention children’s centres (CCs). These are often located in areas of high relative deprivation to support young children. Given the increasing portability of eye-tracking equipment, assessment of large clusters of infants could be undertaken in centres by suitably trained early intervention staff. Here, we report on a study involving 174 infants and their parents, carried out in partnership with CCs, exploring the feasibility of this approach. We report the processes of setting up the project and participant recruitment. We report the diversity of sample obtained on the engagement of CC staff in training and the process of assessment itself.We report the quality of the data obtained, and the levels of engagement of parents and infants. We conclude that this approach has great potential for recruiting large and diverse samples worldwide, provides sufficiently reliable data and is engaging to staff, parents and infants.

An Empirical Evaluation of the Reversal Theory State Measure: Tracking Changes in the Experience of Video Commercials of Three Leading Running Brands

Abstract of paper delivered at the 17th International Reversal Theory Conference, Day 3, session 4, 01.07.15

A Comparative Study of a Low Doppler Shift in a Carrier Tracking Loop for GPS

This paper compares a carrier tracking scenario when a received Global Positioning System (GPS) signal has low Doppler frequency. It is shown that if the Numerically Controlled Oscillator (NCO) is quantized to 1 bit, the carrier tracking loop is unable to keep track of the incoming signal which leaves the tracking loop oscillating between the upper and lower bounds of the tracking loop bandwidth. One way of overcoming this problem is presented and compared with another existing solution, found in the literature, providing comparative results from the use of real-recorded off the air GPS L1 signals. Results show that the proposed method performs better tracking performance compared with the existing solution which it requires much less hardware complexity.

Use of negative information in positioning and tracking algorithms

To avoid additional hardware deployment, indoor localization systems have to be designed in such a way that they rely on existing infrastructure only. Besides the processing of measurements between nodes, localization procedure can include the information of all available environment information. In order to enhance the performance of Wi-Fi based localization systems, the innovative solution presented in this paper considers also the negative information. An indoor tracking method inspired by Kalman filtering is also proposed.

Tracking error of exchange-traded funds: Evidence from the UK

This paper is mainly concerned with the tracking accuracy of Exchange Traded Funds (ETFs) listed on the London Stock Exchange (LSE) but also evaluates their performance and pricing efficiency. The findings show that ETFs offer virtually the same return but exhibit higher volatility than their benchmark. It seems that the pricing efficiency, which should come from the creation and redemption process, does not fully hold as equity ETFs show consistent price premiums. The tracking error of the funds is generally small and is decreasing over time. The risk of the ETF, daily price volatility and the total expense ratio explain a large part of the tracking error. Trading volume, fund size, bid-ask spread and average price premium or discount did not have an impact on the tracking error. Finally, it is concluded that market volatility and the tracking error are positively correlated.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.