Season of drought affects growth, but not nonstructural carbohydrates dynamics, in Pinus taeda saplings.

In temperate evergreen conifers, growth occurs mostly in summer but photosynthesis precedes year-round; thus, nonstructural carbohydrates (NSC) increase in winter but decrease in summer. Given that mild drought reduces growth but not photosynthesis, a drought in summer should increase NSCs more than one in winter. However, the active regulation hypothesis suggests that to increase future drought resilience, plants might downregulate growth to increase NSCs after a winter drought even if NSCs do not increase during the drought. To test if so, potted Pinus taeda saplings (age $< 1$ yr) were subjected to six-month droughts in a greenhouse with one treatment receiving drought during winter (Sep-Mar), and another during summer (Mar-Sep). Both treatments were compared to a control. To measure dry biomass and NSCs, we harvested plants monthly following each drought, while to assess changes in growth rates, we measured height and diameter monthly. While we observed seasonal variation and an overall increase during the study, we found no drought-related changes in NSC dynamics; however, drought did reduce growth. Furthermore, drought in winter did reduce growth during the following summer, but the reduction was less than for a drought in summer. We conclude that the effect of drought on NSCs was too small to detect in our plants. While better control of soil water would have reduced a major source of uncertainty, plants with larger NSC reserves or more intense stress would also yield easier-to-detect effects. Although not definitive, our results suggest that water stress does not lead to dramatic changes in seasonal NSC dynamics in its aftermath, despite what one might expect under the active regulation hypothesis.

Modeling starch dynamics from seasonal variations of photosynthesis, growth, and respiration.

Nonstructural carbohydrates (NSCs) buffer differences in plant carbon supply (photosynthesis) and demand (respiration, growth, etc.) but the regulation of their dynamics remains unresolved. Seasonal variations in NSCs are well-documented, but differences in the time-average, amplitude, phase, and other characteristics across ecosystems and functional types lack explanation; furthermore, observed dynamics do not always match expectations. The failure to match observed and expected dynamics has stimulated debate on whether carbon supply or demand drives NSC dynamics. To gain insight into how carbon supply and demand drive seasonal NSC dynamics, we derive a simple model of NSC dynamics based on carbon mass balance and linearizing the NSC demand to determine how supply-driven and demand-driven seasonal NSC dynamics differ. We find that supply-driven and demand-driven dynamics yield distinct timings of seasonal extrema, and supply overrides demand when carbon supply is low in winter (e.g., at high latitudes). Our results also suggest that NSC dynamics often lag changes carbon mass balance. We also predict differences in NSC dynamics across mass, suggesting saplings are more dynamics and respond faster to the environment than mature trees. Our findings suggest substrate-dependent regulation with environmental variation is sufficient to generate complex NSC dynamics.

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling.

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

Dispensing error rate after implementation of an automated pharmacy carousel system.

PURPOSE: A study was conducted to determine filling and dispensing error rates before and after the implementation of an automated pharmacy carousel system (APCS). METHODS: The study was conducted in a 613-bed acute and tertiary care university hospital. Before the implementation of the APCS, filling and dispensing rates were recorded during October through November 2004 and January 2005. Postimplementation data were collected during May through June 2006. Errors were recorded in three areas of pharmacy operations: first-dose or missing medication fill, automated dispensing cabinet fill, and interdepartmental request fill. A filling error was defined as an error caught by a pharmacist during the verification step. A dispensing error was defined as an error caught by a pharmacist observer after verification by the pharmacist. RESULTS: Before implementation of the APCS, 422 first-dose or missing medication orders were observed between October 2004 and January 2005. Independent data collected in December 2005, approximately six weeks after the introduction of the APCS, found that filling and error rates had increased. The filling rate for automated dispensing cabinets was associated with the largest decrease in errors. Filling and dispensing error rates had decreased by December 2005. In terms of interdepartmental request fill, no dispensing errors were noted in 123 clinic orders dispensed before the implementation of the APCS. One dispensing error out of 85 clinic orders was identified after implementation of the APCS. CONCLUSION: The implementation of an APCS at a university hospital decreased medication filling errors related to automated cabinets only and did not affect other filling and dispensing errors.