Do changes in carbon allocation account for the growth response to potassium and sodium applications in tropical Eucalyptus plantations?

Understanding the underlying mechanisms that account for the impact of potassium (K) fertilization and its replacement by sodium (Na) on tree growth is key to improving the management of forest plantations that are expanding over weathered tropical soils with low amounts of exchangeable bases. A complete randomized block design was planted with Eucalyptus grandis (W. Hill ex Maiden) to quantify growth, carbon uptake and carbon partitioning using a carbon budget approach. A combination of approaches including the establishment of allometric relationships over the whole rotation and measurements of soil CO2 efflux and aboveground litterfall at the end of the rotation were used to estimate aboveground net production (ANPP), total belowground carbon flux and gross primary production (GPP). The stable carbon isotope (delta C-13) of stem wood alpha-cellulose produced every year was used as a proxy for stomatal limitation of photosynthesis. Potassium fertilization increased GPP and decreased the fraction of carbon allocated belowground. Aboveground net production was strongly enhanced, and because leaf lifespan increased, leaf biomass was enhanced without any change in leaf production, and wood production (P-W) was dramatically increased. Sodium application decreased the fraction of carbon allocated belowground in a similar way, and enhanced GPP, ANPP and P-W, but to a lesser extent compared with K fertilization. Neither K nor Na affected delta C-13 of stem wood alpha-cellulose, suggesting that water-use efficiency was the same among the treatments and that the inferred increase in leaf photosynthesis was not only related to a higher stomatal conductance. We concluded that the response to K fertilization and Na addition on P-W resulted from drastic changes in carbon allocation.

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment.