[1] Deep stratosphere-to-troposphere transport (STT) conveying ozone-rich stratospheric air to the lower troposphere in the extratropics can episodically increase ozone concentrations in the lower troposphere. However, dynamical aspects of the descent, including dispersion and mixing with the surrounding tropospheric air and necessary conditions for reaching the lower troposphere, are not clearly understood yet. This study focuses on August 2006, as daily balloon sonde measurements were made from many sites covering North America within the Intercontinental Chemical Transport Experiment Ozonesonde Network Study campaign. During this period, four profiles were found with clear signs of deep STT. A mesoscale model was used together with trajectory calculation to represent these events. Over 10 days, 20 distinct clusters of trajectories were identified as significant deep STT events, including three observed. The four largest clusters carried 41, 35, 25, and 16 × 10 12 kg of mass of air, respectively. A dynamical analysis was performed on the three observed events that were captured numerically. The descents showed three distinct phases: (1) crossing of the tropopause, (2) free descent, and (3) quasi-horizontal dispersion in the lower troposphere. Clusters are rapidly sliding down sloping isentropes while being slowly diabatically cooled (approximately −1 K d −1 ). The tilt in the isentropes along the descent is due to an approximately equal combination of a negative potential temperature anomaly at the tropopause during phase 1 and a nearby baroclinic zone at the ground. The combination of these two conditions appears to be necessary for reaching the lower troposphere. In the three cases, the clusters stayed compact until they reach the lower troposphere, and it is estimated that approximately 80% of the ozone of stratospheric origin is released directly in the lower troposphere.

A new Monte-Carlo based formalism was described and used to assess the variability of sites of potential recurrence predicted by the proliferation-invasion model to input parameter values. The authors have shown that high risk areas could be consistently identified with a limited number of sets (N ≲ 400) of randomly chosen parameter values. A major strength of this formalism is its potential prospective nature. Although a validation of the accuracy of the model-predicted tumor recurrence location still remains to be done, our method is potentially applicable to orient patient-specific definition of margins.

Purpose: Patients diagnosed with glioblastoma multiforme (GBM), the most aggressive form of brain tumour, have a median survival of 15 months when receiving standard treatment. We have developed a three‐dimensional reaction‐diffusion model which uses patient‐specific diffusion tensor imaging (DTI) and real dose distributions to simulate the growth and radiotherapy treatment of a GBM. We highlight paths for tumour recurrence and predict a gain in survival when treatment margins are adjusted according to model inputs. Methods: A DTI sequence is added to pre‐ and post‐treatment MRI for ten GBM patients receiving standard treatment in our department. Our numerical model uses clinically available images to initialize the tumour cell density and to measure invasion velocity. Patient‐ specific DTI is used to model tumour cell motility and to prioritize migration along white matter fibres. The treatment dose distribution is used to simulate the radiotherapy treatment actually received by the patient. Finally, we simulate an alternative treatment plan that increases the dose in the region where the model predicts the formation of a recurrent tumour. Results: The general behaviour of the model was evaluated and found to be adequate for a patient with no DTI but whose post‐treatment images were available. For another patient, model initialization with pre‐ treatment DTI shows that a second tumour focus forms outside the original radiation field in a region where the tumour migration is high due to white matter fibres. The application of an alternative virtual plan integrating the location of the recurrence suggests a 2‐month gain of survival time, based on tumour cell density. Conclusions: Our results indicate that a reaction‐ diffusion model using patient‐specific DTI information could potentially be used to modify GBM treatment margins, leading to an increased survival time. Integrating a long‐term outcome study will allow us to verify the predictive efficiency of the model. This work is funded by the Fonds quebecois de la recherche sur la nature et les technologies (FQRNT) and by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Sheep antibodies to bovine type I collagen were employed in the immunohistochemical detection of type I collagen in lung tissue sections of irradiated LAF1 mice. A video image digitizing system was developed to estimate collagen levels, by assigning a numerical value (0-63) to each of approximately 53,800 picture elements (pixels) in the microscope field, according to the collagen-dependent fluorescence intensity at each locus. For lungs harvested 52 weeks subsequent to graded doses of 60Co gamma radiation between 0 and 10 Gy, a dose-dependent increase in type I collagen was observed in the alveolar walls. A reproducible increase was evident for doses as low as 5 Gy: doses of 7 to 10 Gy elicited type I collagen levels significantly elevated with respect to those of age-matched controls. These results are consistent with a role for type I collagen in the development of radiation-induced pulmonary fibrosis. The assay system developed here will be used to explore the role of connective tissue macromolecules in the development of radiation pneumonitis and fibrosis.

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