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Monte Carlo (MC)-based dose calculations are generally superior to analytical dose calculations (ADC) in modeling the dose distribution for proton pencil beam scanning (PBS) treatments. The purpose of this paper is to present a methodology for commissioning and validating an accurate MC code for PBS utilizing a parameterized source model, including an implementation of a range shifter, that can independently check the ADC in commercial treatment planning system (TPS) and fast Monte Carlo dose calculation in opensource platform (MCsquare). The source model parameters (including beam size, angular divergence and energy spread) and protons per MU were extracted and tuned at the nozzle exit by comparing Tool for Particle Simulation (TOPAS) simulations with a series of commissioning measurements using scintillation screen/CCD camera detector and ionization chambers. The range shifter was simulated as an independent object with geometric and material information. The MC calculation platform was validated through comprehensive measurements of single spots, field size factors (FSF) and three-dimensional dose distributions of spread-out Bragg peaks (SOBPs), both without and with the range shifter. Differences in field size factors and absolute output at various depths of SOBPs between measurement and simulation were within 2.2%, with and without a range shifter, indicating an accurate source model. TOPAS was also validated against anthropomorphic lung phantom measurements. Comparison of dose distributions and DVHs for representative liver and lung cases between independent MC and analytical dose calculations from a commercial TPS further highlights the limitations of the ADC in situations of highly heterogeneous geometries. The fast MC platform has been implemented within our clinical practice to provide additional independent dose validation/QA of the commercial ADC for patient plans. Using the independent MC, we can more efficiently commission ADC by reducing the amount of

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.

Monte Carlo (MC)-based dose calculations are generally superior to analytical dose calculations (ADC) in modeling the dose distribution for proton pencil beam scanning (PBS) treatments. The purpose of this paper is to present a methodology for commissioning and validating an accurate MC code for PBS utilizing a parameterized source model, including an implementation of a range shifter, that can independently check the ADC in commercial treatment planning system (TPS) and fast Monte Carlo dose calculation in opensource platform (MCsquare). The source model parameters (including beam size, angular divergence and energy spread) and protons per MU were extracted and tuned at the nozzle exit by comparing Tool for Particle Simulation (TOPAS) simulations with a series of commissioning measurements using scintillation screen/CCD camera detector and ionization chambers. The range shifter was simulated as an independent object with geometric and material information. The MC calculation platform was validated through comprehensive measurements of single spots, field size factors (FSF) and three-dimensional dose distributions of spread-out Bragg peaks (SOBPs), both without and with the range shifter. Differences in field size factors and absolute output at various depths of SOBPs between measurement and simulation were within 2.2%, with and without a range shifter, indicating an accurate source model. TOPAS was also validated against anthropomorphic lung phantom measurements. Comparison of dose distributions and DVHs for representative liver and lung cases between independent MC and analytical dose calculations from a commercial TPS further highlights the limitations of the ADC in situations of highly heterogeneous geometries. The fast MC platform has been implemented within our clinical practice to provide additional independent dose validation/QA of the commercial ADC for patient plans. Using the independent MC, we can more efficiently commission ADC by reducing the amount of

At a national level it is common to express the amount of air travel in terms of the numberof revenue passenger miles flown or the number of enplaned passengers. This provides a way toresolve the difficulty of how to aggregate measures of air travel in many different markets ofmany different distances. However, information about the distribution of trip lengths is lost in theprocess.This information is of interest for a number of reasons. The type of aircraft that is mostappropriate for different markets depends on the distances involved. The length of the trip is alsolikely to influence traveler behavior in terms of the importance of convenient access to airports,frequency of service, and willingness to make intermediate stops.A related issue is how the cost of air travel varies with the length of the trip. The coststructure of airline service is such that the cost per mile flown reduces with increasing trip length,and this is reflected in typical airline fares. However, other factors also influence fares, such as airline competition, and the fare structure may or may not reflect the costs involved for trips ofdifferent lengths.Therefore, this study examines the distribution of trip lengths and associated average faresin the U.S. domestic air travel market. For the purpose of this study, trip length is defined as thedirect distance between the origin and destination of a trip. In practice, passengers may fly viaintermediate stops, as when they take a connecting flight through an airline hub, and this will generally increase the distance flown. However, this aspect of trip length is not considered here.

At a national level it is common to express the amount of air travel in terms of the numberof revenue passenger miles flown or the number of enplaned passengers. This provides a way toresolve the difficulty of how to aggregate measures of air travel in many different markets ofmany different distances. However, information about the distribution of trip lengths is lost in theprocess.This information is of interest for a number of reasons. The type of aircraft that is mostappropriate for different markets depends on the distances involved. The length of the trip is alsolikely to influence traveler behavior in terms of the importance of convenient access to airports,frequency of service, and willingness to make intermediate stops.A related issue is how the cost of air travel varies with the length of the trip. The coststructure of airline service is such that the cost per mile flown reduces with increasing trip length,and this is reflected in typical airline fares. However, other factors also influence fares, such as airline competition, and the fare structure may or may not reflect the costs involved for trips ofdifferent lengths.Therefore, this study examines the distribution of trip lengths and associated average faresin the U.S. domestic air travel market. For the purpose of this study, trip length is defined as thedirect distance between the origin and destination of a trip. In practice, passengers may fly viaintermediate stops, as when they take a connecting flight through an airline hub, and this will generally increase the distance flown. However, this aspect of trip length is not considered here.