The carbohydrate content of the tomato fruit is a major determinant of the quality and value of the crop, whether it be for the fresh produce market or for processing. Soluble sugar levels contribute strongly to the soluble solids content and to tomato flavor (Stevens et al., 1977a, 1977b) and, therefore, increasing these levels has been the goal of many research efforts. The carbohydrate economy of developing tomato fruit is determined by the whole gamut of plant source-sink relationships. These include photoassimilate production at the source, its partitioning within the leaf, transport and export to alternative sinks and, finally, import into and metabolism within the fruit sink. Although the carbohydrate status of the fruit is a product of the interactions among all these processes, the fate of imported photoassimilate partitioned to the fruit is controlled only by the carbohydrate metabolism in the fruit itself. Clearly, fruit carbohydrate content can be increased by improving the source contribution to the carbohydrate economy. This can be seen, for example, in the effects on fruit total soluble solids (TSS) content of: light intensity; plant architecture modifications, such as by pruning; or genetic control of growth habit (sp gene) (see Davies and Hobson, 1981). The same is likely to apply to the effects of the physiological components of loading, transport, and unloading processes on fruit sugar content. Recent studies of these processes in tomatoes (Ruan and Patrick, 1995; Ruan et al., 1997) should help to set the stage for the

The carbohydrate economy of developing tomato fruit is determined by wholeplant source–sink relationships. However, the fate of the imported photoassimilate partitioned to the fruit sink is controlled by the carbohydrate metabolism of the fruit tissue. Within the Lycopersicon spp. there exists a broad range of genetic variability for fruit carbohydrate metabolism, such as sucrose accumulation and modified ratios of fructose to glucose in the mature fruit and increased starch synthesis in the immature fruit. Metabolic pathways of carbohydrate metabolism in tomatoes, as well as natural genetic variation in the metabolic pathways, will be described. The impact of sink carbohydrate metabolism on fruit non-structural carbohydrate economy will be discussed.

Purpose The GammaPod is a novel device for stereotactic breast treatments that employs 25 rotating Co‐60 sources while the patient is continuously translated in three axes to deliver a highly conformal dose to the target. There is no commercial software available for independent second calculations. The purpose of this study is to determine an efficient way to estimate GammaPod treatment times based on target volume and use it as a second calculation for patient‐specific quality assurance. Methods Fifty‐nine GammaPod (Xcision Medical Systems, LLC.) breast cancer patient treatments were used as the fitting dataset for this study. Similar to the Curie‐seconds concept in brachytherapy, we considered dose‐rate × time/(prescribed dose) as a function of target volumes. Using a MATLAB (Mathworks, Natick, MA, USA) script, we generated linear (with 95% confidence interval (CI)) and quadratic fits and tested the resulting equations on an additional set of 30 patients. Results We found a strong correlation between the dose‐rate × time/(prescribed dose) and patients’ target volumes for both the linear and quadratic models. The linear fit was selected for use and using the polyval function in MATLAB, a 95% CI graph was created to depict the accuracy of the prediction for treatment times. Testing the model on 30 additional patients with target volumes ranging from 20 to 188 cc yielded treatment times from 10 to 25 min that in all cases were within the predicted CI. The average absolute difference between the predicted and actual treatment times was 1.0 min (range 0–3.3 min). The average percent difference was 5.8% (range 0%–18.4%). Conclusion This work has resulted in a viable independent calculation for GammaPod treatment times. This method has been implemented as a spreadsheet that is ready for clinical use to predict and verify the accuracy of breast cancer treatment times.