A g-factor metric for k-t-GRAPPA- and PEAK-GRAPPA-based parallel imaging.

PURPOSE The aim of this work is to derive a theoretical framework for quantitative noise and temporal fidelity analysis of time-resolved k-space-based parallel imaging methods. THEORY An analytical formalism of noise distribution is derived extending the existing g-factor formulation for nontime-resolved generalized autocalibrating partially parallel acquisition (GRAPPA) to time-resolved k-space-based methods. The noise analysis considers temporal noise correlations and is further accompanied by a temporal filtering analysis. METHODS All methods are derived and presented for k-t-GRAPPA and PEAK-GRAPPA. A sliding window reconstruction and nontime-resolved GRAPPA are taken as a reference. Statistical validation is based on series of pseudoreplica images. The analysis is demonstrated on a short-axis cardiac CINE dataset. RESULTS The superior signal-to-noise performance of time-resolved over nontime-resolved parallel imaging methods at the expense of temporal frequency filtering is analytically confirmed. Further, different temporal frequency filter characteristics of k-t-GRAPPA, PEAK-GRAPPA, and sliding window are revealed. CONCLUSION The proposed analysis of noise behavior and temporal fidelity establishes a theoretical basis for a quantitative evaluation of time-resolved reconstruction methods. Therefore, the presented theory allows for comparison between time-resolved parallel imaging methods and also nontime-resolved methods. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.

Pulse-shaping assisted multidimensional coherent electronic spectroscopy

Understanding nuclear and electronic dynamics of molecular systems has advanced considerably by probing their nonlinear responses with a suitable sequence of pulses. Moreover, the ability to control crucial parameters of the excitation pulses, such as duration, sequence, frequency, polarization, slowly varying envelope, or carrier phase, has led to a variety of advanced time-resolved spectroscopic methodologies. Recently, two-dimensional electronic spectroscopy with ultrashort pulses has become a more and more popular tool since it allows to obtain information on energy and coherence transfer phenomena, line broadening mechanisms, or the presence of quantum coherences in molecular complexes. Here, we present a high fidelity two-dimensional electronic spectroscopy setup designed for molecular systems in solution. It incorporates the versatility of pulse-shaping methods to achieve full control on the amplitude and phase of the individual exciting and probing pulses. Selective and precise amplitude- and phase-modulation is shown and applied to investigate electronic dynamics in several reference molecular systems.

Solvation-Driven Charge Transfer and Localization in Metal Complexes

In any physicochemical process in liquids, the dynamical response of the solvent to the solutes out of equilibrium plays a crucial role in the rates and products: the solvent molecules react to the changes in volume and electron density of the solutes to minimize the free energy of the solution, thus modulating the activation barriers and stabilizing (or destabilizing) intermediate states. In charge transfer (CT) processes in polar solvents, the response of the solvent always assists the formation of charge separation states by stabilizing the energy of the localized charges. A deep understanding of the solvation mechanisms and time scales is therefore essential for a correct description of any photochemical process in dense phase and for designing molecular devices based on photosensitizers with CT excited states. In the last two decades, with the advent of ultrafast time-resolved spectroscopies, microscopic models describing the relevant case of polar solvation (where both the solvent and the solute molecules have a permanent electric dipole and the mutual interaction is mainly dipole−dipole) have dramatically progressed. Regardless of the details of each model, they all assume that the effect of the electrostatic fields of the solvent molecules on the internal electronic dynamics of the solute are perturbative and that the solvent−solute coupling is mainly an electrostatic interaction between the constant permanent dipoles of the solute and the solvent molecules. This well-established picture has proven to quantitatively rationalize spectroscopic effects of environmental and electric dynamics (time-resolved Stokes shifts, inhomogeneous broadening, etc.). However, recent computational and experimental studies, including ours, have shown that further improvement is required. Indeed, in the last years we investigated several molecular complexes exhibiting photoexcited CT states, and we found that the current description of the formation and stabilization of CT states in an important group of molecules such as transition metal complexes is inaccurate. In particular, we proved that the solvent molecules are not just spectators of intramolecular electron density redistribution but significantly modulate it. Our results solicit further development of quantum mechanics computational methods to treat the solute and (at least) the closest solvent molecules including the nonperturbative treatment of the effects of local electrostatics and direct solvent−solute interactions to describe the dynamical changes of the solute excited states during the solvent response.

A STUDY OF INFLUENZA AND OF STATISTICAL METHODS USED TO REPORT INFLUENZA

This paper defines and compares several models for describing excess influenza pneumonia mortality in Houston. First, the methodology used by the Center for Disease Control is examined and several variations of this methodology are studied. All of the models examined emphasize the difficulty of omitting epidemic weeks.^ In an attempt to find a better method of describing expected and epidemic mortality, time series methods are examined. Grouping in four-week periods, truncating the data series to adjust epidemic periods, and seasonally-adjusting the series y(,t), by:^ (DIAGRAM, TABLE OR GRAPHIC OMITTED...PLEASE SEE DAI)^ is the best method examined. This new series w(,t) is stationary and a moving average model MA(1) gives a good fit for forecasting influenza and pneumonia mortality in Houston.^ Influenza morbidity, other causes of death, sex, race, age, climate variables, environmental factors, and school absenteeism are all examined in terms of their relationship to influenza and pneumonia mortality. Both influenza morbidity and ischemic heart disease mortality show a very high relationship that remains when seasonal trends are removed from the data. However, when jointly modeling the three series it is obvious that the simple time series MA(1) model of truncated, seasonally-adjusted four-week data gives a better forecast.^

Mg/Ca ratios in planktonic foraminifera

The primary Mg/Ca ratio of foraminiferal shells is a potentially valuable paleoproxy for sea surface temperature (SST) reconstructions. However, the reliable extraction of this ratio from sedimentary calcite assumes that we can overcome artifacts related to foraminiferal ecology and partial dissolution, as well as contamination by secondary calcite and clay. The standard batch method for Mg/Ca analysis involves cracking, sonicating, and rinsing the tests to remove clay, followed by chemical cleaning, and finally acid-digestion and single-point measurement. This laborious procedure often results in substantial loss of sample (typically 30-60%). We find that even the earliest steps of this procedure can fractionate Mg from Ca, thus biasing the result toward a more variable and often anomalously low Mg/Ca ratio. Moreover, the more rigorous the cleaning, the more calcite is lost, and the more likely it becomes that any residual clay that has not been removed by physical cleaning will increase the ratio. These potentially significant sources of error can be overcome with a flow-through (FT) sequential leaching method that makes time- and labor-intensive pretreatments unnecessary. When combined with time-resolved analysis (FT-TRA) flow-through, performed with a gradually increasing and highly regulated acid strength, produces continuous records of Mg, Sr, Al, and Ca concentrations in the leachate sorted by dissolution susceptibility of the reacting material. Flow-through separates secondary calcite from less susceptible biogenic calcite and clay, and further resolves the biogenic component into primary and more resistant fractions. FT-TRA reliably separates secondary calcite (which is not representative of original life habitats) from the more resistant biogenic calcite (the desired signal) and clay (a contaminant of high Mg/Ca, which also contains Al), and further resolves the biogenic component into primary and more resistant fractions that may reflect habitat or other changes during ontogeny. We find that the most susceptible fraction of biogenic calcite in surface dwelling foraminifera gives the most accurate value for SST and therefore best represents primary calcite. Sequential dissolution curves can be used to correct the primary Mg/Ca ratio for clay, if necessary. However, the temporal separation of calcite from clay in FT-TRA is so complete that this correction is typically <=2%, even in clay-rich sediments. Unlike hands-on batch methods, that are difficult to reproduce exactly, flow-through lends itself to automation, providing precise replication of treatment for every sample. Our automated flow-through system can process 22 samples, two system blanks, and 48 mixed standards in <12 hours of unattended operation. FT-TRA thus represents a faster, cheaper, and better way to determine Mg/Ca ratios in foraminiferal calcite.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Advanced data acquisition system implementation for the ITER Neutron Diagnostic use case using EPICS and FlexRIO technology on a PXIe platform

In the framework of the ITER Control Breakdown Structure (CBS), Plant System Instrumentation & Control (I&C) defines the hardware and software required to control one or more plant systems [1]. For diagnostics, most of the complex Plant System I&C are to be delivered by ITER Domestic Agencies (DAs). As an example for the DAs, ITER Organization (IO) has developed several use cases for diagnostics Plant System I&C that fully comply with guidelines presented in the Plant Control Design Handbook (PCDH) [2]. One such use case is for neutron diagnostics, specifically the Fission Chamber (FC), which is responsible for delivering time-resolved measurements of neutron source strength and fusion power to aid in assessing the functional performance of ITER [3]. ITER will deploy four Fission Chamber units, each consisting of three individual FC detectors. Two of these detectors contain Uranium 235 for Neutron detection, while a third "dummy" detector will provide gamma and noise detection. The neutron flux from each MFC is measured by the three methods: . Counting Mode: measures the number of individual pulses and their location in the record. Pulse parameters (threshold and width) are user configurable. . Campbelling Mode (Mean Square Voltage): measures the RMS deviation in signal amplitude from its average value. .Current Mode: integrates the signal amplitude over the measurement period

Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging

Functional neuroimaging studies in human subjects using positron emission tomography or functional magnetic resonance imaging (fMRI) are typically conducted by collecting data over extended time periods that contain many similar trials of a task. Here methods for acquiring fMRI data from single trials of a cognitive task are reported. In experiment one, whole brain fMRI was used to reliably detect single-trial responses in a prefrontal region within single subjects. In experiment two, higher temporal sampling of a more limited spatial field was used to measure temporal offsets between regions. Activation maps produced solely from the single-trial data were comparable to those produced from blocked runs. These findings suggest that single-trial paradigms will be able to exploit the high temporal resolution of fMRI. Such paradigms will provide experimental flexibility and time-resolved data for individual brain regions on a trial-by-trial basis.

Monitoring the GAP catalyzed H-Ras GTPase reaction at atomic resolution in real time

The molecular reaction mechanism of the GTPase-activating protein (GAP)-catalyzed GTP hydrolysis by Ras was investigated by time resolved Fourier transform infrared (FTIR) difference spectroscopy using caged GTP (P3-1-(2-nitro)phenylethyl guanosine 5′-O-triphosphate) as photolabile trigger. This approach provides the complete GTPase reaction pathway with time resolution of milliseconds at the atomic level. Up to now, one structural model of the GAP⋅Ras⋅GDP⋅AlFx transition state analog is known, which represents a “snap shot” along the reaction-pathway. As now revealed, binding of GAP to Ras⋅GTP shifts negative charge from the γ to β phosphate. Such a shift was already identified by FTIR in GTP because of Ras binding and is now shown to be enhanced by GAP binding. Because the charge distribution of the GAP⋅Ras⋅GTP complex thus resembles a more dissociative-like transition state and is more like that in GDP, the activation free energy is reduced. An intermediate is observed on the reaction pathway that appears when the bond between β and γ phosphate is cleaved. In the intermediate, the released Pi is strongly bound to the protein and surprisingly shows bands typical of those seen for phosphorylated enzyme intermediates. All these results provide a mechanistic picture that is different from the intrinsic GTPase reaction of Ras. FTIR analysis reveals the release of Pi from the protein complex as the rate-limiting step for the GAP-catalyzed reaction. The approach presented allows the study not only of single proteins but of protein–protein interactions without intrinsic chromophores, in the non-crystalline state, in real time at the atomic level.

Lead sulfide nanocrystal: conducting polymer solar cells

In this paper, we report photovoltaic devices fabricated from lead sulfide nanocrystals and the conducting polymer poly(2-methoxy-5-(2'-ethyl-hexyloxy)-p-phenylene vinylene). This composite material was produced via a new single-pot synthesis which solves many of the issues associated with existing methods. Our devices have white light power conversion efficiencies under AM 1.5 illumination of 0.7% and single wavelength conversion efficiencies of 1.1%. Additionally, they exhibit remarkably good ideality factors (n = 1.15). Our measurements show that these composites have significant potential as soft optoelectronic materials.

Occupational sitting time and overweight and obesity in Australian workers

Background: One of the major immediate and long-term health issues in modern society is the problem of overweight and obesity. This paper examines the role of the workplace in the problem by studying the association between occupational sitting time and overweight and obesity (body mass index [BMI] >= 25) in a sample of adult Australians in full-time employment. Methods: Data on age, gender, occupation, physical activity, occupational sitting time, and BMI were collected in September 2003 from a sample of 1579 adult men and women in full-time employment at the time of the survey. Logistic regression was used to examine the association between occupational sitting time and overweight and obesity. Results: Mean occupational sitting time was > 3 hours/day, and significantly higher in men (209 minutes) than in women (189 minutes, p =0.026). Univariate analyses showed significant associations between occupational sitting time and BMI of >= 25 in men but not in women. After adjusting for age, occupation, and physical activity, the odds ratio for BMI >= 25 was 1.92 (confidence interval: 1.17-3.17) in men who reported sitting for > 6 hours/day, compared with those who sat for < 45 minutes/day. Conclusions: Occupational sitting time was independently associated with overweight and obesity in men who were in full-time paid work. These results suggest that the workplace may play an important role in the growing problem of overweight and obesity. Further research is needed to clearly understand the association between sitting time at work and over-weight and obesity in women.

Effective cell-centred time-domain Maxwell's equations numerical solvers

This research work analyses techniques for implementing a cell-centred finite-volume time-domain (ccFV-TD) computational methodology for the purpose of studying microwave heating. Various state-of-the-art spatial and temporal discretisation methods employed to solve Maxwell's equations on multidimensional structured grid networks are investigated, and the dispersive and dissipative errors inherent in those techniques examined. Both staggered and unstaggered grid approaches are considered. Upwind schemes using a Riemann solver and intensity vector splitting are studied and evaluated. Staggered and unstaggered Leapfrog and Runge-Kutta time integration methods are analysed in terms of phase and amplitude error to identify which method is the most accurate and efficient for simulating microwave heating processes. The implementation and migration of typical electromagnetic boundary conditions. from staggered in space to cell-centred approaches also is deliberated. In particular, an existing perfectly matched layer absorbing boundary methodology is adapted to formulate a new cell-centred boundary implementation for the ccFV-TD solvers. Finally for microwave heating purposes, a comparison of analytical and numerical results for standard case studies in rectangular waveguides allows the accuracy of the developed methods to be assessed. © 2004 Elsevier Inc. All rights reserved.

A time-harmonic target-field method for designing unshielded RF coils in MRI

Time-harmonic methods are required in the accurate design of RF coils as operating frequency increases. This paper presents such a method to find a current density solution on the coil that will induce some desired magnetic field upon an asymmetrically located target region within. This inverse method appropriately considers the geometry of the coil via a Fourier series expansion, and incorporates some new regularization penalty functions in the solution process. A new technique is introduced by which the complex, time-dependent current density solution is approximated by a static coil winding pattern. Several winding pattern solutions are given, with more complex winding patterns corresponding to more desirable induced magnetic fields.

Asymptotic theory of time varying networks with memory and heterogeneous activation pattern

In this thesis work we develop a new generative model of social networks belonging to the family of Time Varying Networks. The importance of correctly modelling the mechanisms shaping the growth of a network and the dynamics of the edges activation and inactivation are of central importance in network science. Indeed, by means of generative models that mimic the real-world dynamics of contacts in social networks it is possible to forecast the outcome of an epidemic process, optimize the immunization campaign or optimally spread an information among individuals. This task can now be tackled taking advantage of the recent availability of large-scale, high-quality and time-resolved datasets. This wealth of digital data has allowed to deepen our understanding of the structure and properties of many real-world networks. Moreover, the empirical evidence of a temporal dimension in networks prompted the switch of paradigm from a static representation of graphs to a time varying one. In this work we exploit the Activity-Driven paradigm (a modeling tool belonging to the family of Time-Varying-Networks) to develop a general dynamical model that encodes fundamental mechanism shaping the social networks' topology and its temporal structure: social capital allocation and burstiness. The former accounts for the fact that individuals does not randomly invest their time and social interactions but they rather allocate it toward already known nodes of the network. The latter accounts for the heavy-tailed distributions of the inter-event time in social networks. We then empirically measure the properties of these two mechanisms from seven real-world datasets and develop a data-driven model, analytically solving it. We then check the results against numerical simulations and test our predictions with real-world datasets, finding a good agreement between the two. Moreover, we find and characterize a non-trivial interplay between burstiness and social capital allocation in the parameters phase space. Finally, we present a novel approach to the development of a complete generative model of Time-Varying-Networks. This model is inspired by the Kaufman's adjacent possible theory and is based on a generalized version of the Polya's urn. Remarkably, most of the complex and heterogeneous feature of real-world social networks are naturally reproduced by this dynamical model, together with many high-order topological properties (clustering coefficient, community structure etc.).

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^