Relative and Absolute Quantitation of Metabolites and Lipids using LC/MS/MS on the TSQ Quantum Discovery MAX

Two biological systems were studied using LC/ESI/MS/MS on a triple quadrupole operated in SRM (selected reaction monitoring) scan mode. The first bacterium system is aquatic and microscopic in size known as Roseobacter. The second mammalian system is terrestrial and large in size relative to humans known as Holstein cows. Roseobacter is a clade of marine bacteria abundant in the ocean. Roseophages are viruses that infect Roseobacter and cause viral lysis. Sulfitobacter sp. 2047 was isolated and infected with Roseophages, and the fold change in the metabolic pool relative to a control was studied at discrete time points. The absolute concentration of glutamate and glutamine in the infected and control was determined at each time point using an external calibration curve. Flux analysis through the addition of 13C-acetate at early and late post infection was compared to the control. Holstein cows are a breed of cattle known to be the world’s highest producers of milk. Twelve Holstein dairy cows were selected, and samples of blood and milk were taken at different weeks of lactation. The fold change in the phospholipid pool relative to the first week of lactation was studied from early, mid, and late lactation. The absolute concentration of lipids at each week of lactation was determined using isotope dilution mass spectrometry with the exception of GPC (glycerophosphocholine) where an external calibration curve was used due commercial unavailability of an isotope-labeled standard

Automatic Workflow Monitoring in Industrial Environments

Robust automatic workflow monitoring using visual sensors in industrial environments is still an unsolved problem. This is mainly due to the difficulties of recording data in work settings and the environmental conditions (large occlusions, similar background/foreground) which do not allow object detection/tracking algorithms to perform robustly. Hence approaches analysing trajectories are limited in such environments. However, workflow monitoring is especially needed due to quality and safety requirements. In this paper we propose a robust approach for workflow classification in industrial environments. The proposed approach consists of a robust scene descriptor and an efficient time series analysis method. Experimental results on a challenging car manufacturing dataset showed that the proposed scene descriptor is able to detect both human and machinery related motion robustly and the used time series analysis method can classify tasks in a given workflow automatically

Comparing two measures of mental toughness

This paper tested relations between two measures of mental toughness. A sample of 110 male athletes (M age = 20.81 years; SD = 2.76), derived from University sports teams and local sports clubs, gave informed consent before completing two questionnaires to assess mental toughness. It was hypothesized that scales and subscales from the two different instruments, which purported to measure the same or substantially overlapping scales, would be strongly correlated. Predictions concerning the expected relations were made a priori. Pearson correlations revealed a significant and positive relationship between higher order mental toughness scores (r = .75; p <.001). Correlations between similar mental toughness subscales were found to be positive and significant but somewhat lower than expected (r = .49 to .62). Results suggest instrument subscales with similar labels are not measuring the same components of mental toughness

A smart environment for biometric capture

The development of large scale biometric systems require experiments to be performed on large amounts of data. Existing capture systems are designed for fixed experiments and are not easily scalable. In this scenario even the addition of extra data is difficult. We developed a prototype biometric tunnel for the capture of non-contact biometrics. It is self contained and autonomous. Such a configuration is ideal for building access or deployment in secure environments. The tunnel captures cropped images of the subject's face and performs a 3D reconstruction of the person's motion which is used to extract gait information. Interaction between the various parts of the system is performed via the use of an agent framework. The design of this system is a trade-off between parallel and serial processing due to various hardware bottlenecks. When tested on a small population the extracted features have been shown to be potent for recognition. We currently achieve a moderate throughput of approximate 15 subjects an hour and hope to improve this in the future as the prototype becomes more complete

A middleware for a large array of cameras

Large arrays of cameras are increasingly being employed for producing high quality image sequences needed for motion analysis research. This leads to the logistical problem with coordination and control of a large number of cameras. In this paper, we used a lightweight multi-agent system for coordinating such camera arrays. The agent framework provides more than a remote sensor access API. It allows reconfigurable and transparent access to cameras, as well as software agents capable of intelligent processing. Furthermore, it eases maintenance by encouraging code reuse. Additionally, our agent system includes an automatic discovery mechanism at startup, and multiple language bindings. Performance tests showed the lightweight nature of the framework while validating its correctness and scalability. Two different camera agents were implemented to provide access to a large array of distributed cameras. Correct operation of these camera agents was confirmed via several image processing agents

Airplane takeoff and landing performance monitoring system

The invention is a real-time takeoff and landing performance monitoring system for an aircraft which provides a pilot with graphic and metric information to assist in decisions related to achieving rotation speed (V.sub.R) within the safe zone of a runway, or stopping the aircraft on the runway after landing or take-off abort. The system processes information in two segments: a pretakeoff segment and a real-time segment. One-time inputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data. The real-time segment uses the scheduled performance data, runway length data and transducer measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane acceleration and engine-performance anomalies are detected and annunciated. A novel and important feature of this segment is that it updates the estimated runway rolling friction coefficient. Airplane performance predictions also reflect changes in head wind occurring as the takeoff roll progresses. The system provides a head-down display and a head-up display. The head-up display is projected onto a partially reflective transparent surface through which the pilot views the runway. By comparing the present performance of the airplane with a continually predicted nominal performance based upon given conditions, performance deficiencies are detected by the system and conveyed to pilot in form of both elemental information and integrated information

Uncertainty quantification for CO2 sequestration and enhanced oil recovery

This study develops a statistical method to perform uncertainty quantification for understanding CO2 storage potential within an enhanced oil recovery (EOR) environment at the Farnsworth Unit of the Anadarko Basin in northern Texas. A set of geostatistical-based Monte Carlo simulations of CO2-oil-water flow and reactive transport in the Morrow formation are conducted for global sensitivity and statistical analysis of the major uncertainty metrics: net CO2 injection, cumulative oil production, cumulative gas (CH4) production, and net water injection. A global sensitivity and response surface analysis indicates that reservoir permeability, porosity, and thickness are the major intrinsic reservoir parameters that control net CO2 injection/storage and oil/gas recovery rates. The well spacing and the initial water saturation also have large impact on the oil/gas recovery rates. Further, this study has revealed key insights into the potential behavior and the operational parameters of CO2 sequestration at CO2-EOR sites, including the impact of reservoir characterization uncertainty; understanding this uncertainty is critical in terms of economic decision making and the cost-effectiveness of CO2 storage through EOR.Comment: 9 pages, 6 figures, in press, Energy Procedia, 201