Optical properties and 14C ages of stream DOM from agricultural and forest watersheds during storms.

Forest and agricultural land use affects the concentration and composition of dissolved organic carbon (DOC) in streams and rivers. To elucidate the impacts of forest and agricultural land use on stream DOC during storm events, we investigated DOC concentration ([DOC]), optical properties of dissolved organic matter (DOM), and Δ14C-DOC in both forest- and agriculture-dominated headwater streams in South Korea in the summer of 2012. One forested and five agricultural streams were investigated. During storms, the peak [DOC] of forest stream increased to 5.8 mg L-1, approximately two times larger than that of the most agricultural stream (3.2 mg L-1), demonstrating the weaker storm responses of the [DOC] of agricultural streams to hydrological change. Five PARAFAC components were identified, including three terrestrial humic-like substances (C1, C2, C3), one microbial humic substance (C4), and one microbial protein-like substances (C5). The mean (C4+C5)/(C1+C2+C3) of all storm events at the most agricultural stream was 1.5 times larger than that of the most forested stream, suggesting that more protein-like DOM is exported from agricultural watersheds. Whereas a forest stream was primarily composed of terrestrially derived and 14C-enriched modern DOC, the 14C-age of the most agricultural stream was up to ∼1000 years old. The results suggest that agricultural practices could decrease the old organic carbon pools from soils. However, how quickly the aged DOC can be degraded to CO2 in streams is unknown, warranting future investigation on lability of the aged DOC and their effects on CO2 evasion from rivers and estuaries downstream.

Predictive modeling of lung motion over the entire respiratory cycle using measured pressure-volume data, 4DCT images, and finite-element analysis.

PURPOSE: Predicting complex patterns of respiration can benefit the management of the respiratory motion for radiation therapy of lung cancer. The purpose of the present work was to develop a patient-specific, physiologically relevant respiratory motion model which is capable of predicting lung tumor motion over a complete normal breathing cycle. METHODS: Currently employed techniques for generating the lung geometry from four-dimensional computed tomography data tend to lose details of mesh topology due to excessive surface smoothening. Some of the existing models apply displacement boundary conditions instead of the intrapleural pressure as the actual motive force for respiration, while others ignore the nonlinearity of lung tissues or the mechanics of pleural sliding. An intermediate nonuniform rational basis spline surface representation is used to avoid multiple geometric smoothing procedures used in the computational mesh preparation. Measured chest pressure-volume relationships are used to simulate pressure loading on the surface of the model for a given lung volume, as in actual breathing. A hyperelastic model, developed from experimental observations, has been used to model the lung tissue material. Pleural sliding on the inside of the ribcage has also been considered. RESULTS: The finite-element model has been validated using landmarks from four patient CT data sets over 34 breathing phases. The average differences of end-inspiration in position between the landmarks and those predicted by the model are observed to be 0.450 +/- 0.330 cm for Patient P1, 0.387 +/- 0.169 cm for Patient P2, 0.319 +/- 0.186 cm for Patient P3, and 0.204 +/- 0.102 cm for Patient P4 in the magnitude of error vector, respectively. The average errors of prediction at landmarks over multiple breathing phases in superior-inferior direction are less than 3 mm. CONCLUSIONS: The prediction capability of pressure-volume curve driven nonlinear finite-element model is consistent over the entire breathing cycle. The biomechanical parameters in the model are physiologically measurable, so that the results can be extended to other patients and additional neighboring organs affected by respiratory motion.

Modeling respiratory motion for cancer radiation therapy based on patient-specific 4DCT data.

Prediction of respiratory motion has the potential to substantially improve cancer radiation therapy. A nonlinear finite element (FE) model of respiratory motion during full breathing cycle has been developed based on patient specific pressure-volume relationship and 4D Computed Tomography (CT) data. For geometric modeling of lungs and ribcage we have constructed intermediate CAD surface which avoids multiple geometric smoothing procedures. For physiologically relevant respiratory motion modeling we have used pressure-volume (PV) relationship to apply pressure loading on the surface of the model. A hyperelastic soft tissue model, developed from experimental observations, has been used. Additionally, pleural sliding has been considered which results in accurate deformations in the superior-inferior (SI) direction. The finite element model has been validated using 51 landmarks from the CT data. The average differences in position is seen to be 0.07 cm (SD = 0.20 cm), 0.07 cm (0.15 cm), and 0.22 cm (0.18 cm) in the left-right, anterior-posterior, and superior-inferior directions, respectively.