Simulating organ biomass variability and carbohydrate distribution in perennial fruit crops: a comparison between the common assimilate pool and phloem carbohydrate transport models

Variability in fruit quality greatly impedes the profitability of an orchard. Modelling can help find the causes of quality variability. However, studies suggest that the common assimilate pool model is inadequate in terms of describing variability in organ biomass. The aim of the current study was to compare the performances of the common assimilate pool and phloem carbohydrate transport models in simulating phloem carbohydrate concentration and organ biomass variability within the whole-plant functional-structural grapevine (Vitis vinifera L.) model that we developed previously. A statistical approach was developed for calibrating the model with a detailed potted experiment that entails three levels of leaf area per vine during the fruit ripening period. Global sensitivity analysis illustrated that carbohydrate allocation changed with the amount of leaf area as well as the limiting factors for organ biomass development. Under a homogenous canopy architecture where all grape bunches were equally close to the carbohydrate sources, the common assimilate pool and phloem transport models produced very similar results. However, under a heterogeneous canopy architecture with variable distance between bunches and carbohydrate sources, the coefficient of variation for fruit biomass rose from 0.01 to 0.17 as crop load increased. These results indicate that carbohydrate allocation to fruits is affected by both the size of crop load and fruit distribution, which is not adequately described by the common assimilate pool model. The new grapevine model can also simulate dynamic canopy growth and be adapted to help optimise canopy architecture and quality variability of other perennial fruit crops.

Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss

This study presents a model-based sensitivity analysis of the strength of voluntary muscle contraction with respect to different patterns of motor unit loss. A motor unit pool model was implemented including simulation of a motor neuron pool, muscle force, and surface electromyogram (EMG) signals. Three different patterns of motor unit loss were simulated, including (1) motor unit loss restricted to the largest ones, (2) motor unit loss restricted to the smallest ones, and (3) motor unit loss without size restriction. The model outputs including muscle force amplitude, variability, and the resultant EMG-force relation were quantified under two different motor neuron firing strategies. It was found that motor unit loss restricted to the largest ones had the most dominant impact on muscle strength and significantly changed the EMG-force relation, while loss restricted to the smallest motor units had a pronounced effect on force variability. These findings provide valuable insight toward our understanding of the neurophysiological mechanisms underlying experimental observations of muscle strength, force control, and EMG-force relation in both normal and pathological conditions.

Implications of the Thermodynamic Response of Soil Mineralization, Respiration, and Nitrification on Soil Organic Matter Retention

Considerable research has shown that modifications in global temperature regimes can lead to changes in the interactions between soil respiration and the sequestration of C and N into soil organic matter (SOM). We hypothesized that despite the interconnected nature of respiration, net N mineralization, and nitrification processes, there would be differences in their thermodynamic responses that would affect the composition of inorganic soil N and the potential for retention of N in SOM. To test this hypothesis, soil respiration, N mineralization and nitrification responses were evaluated during constant temperature incubations at seven temperatures (4–42°C) in tilled and no-till soils from two major agroecological zones in Oregon; Willamette Valley, and Pendleton located in the Columbia River Basin. We observed (1) significant thermodynamic differences between the three processes in all soils, (2) a distinctly different thermodynamic profile in Willamette vs. Pendleton, and (3) a dynamic response of Topt (optimal temperature for activity), and Tsmax (temperature of greatest rate response to temperature), and temperature sensitivity (ΔCp‡) over the incubation time course, resulting in shifts in the thermodynamic profiles that could not be adequately explained by changes in process rates. We found that differences in contributions of ammonia oxidizing archaea and bacteria to nitrification activity across temperature helped to explain the thermodynamic differences of this process between Willamette and Pendleton soils. A two-pool model of SOM utilization demonstrated that the dynamic thermodynamic response of respiration in the soils was due to shifts in utilization of labile and less-labile pools of C; and that the respiration response by Pendleton soils was more dependent upon contributions from the less-labile C pool resulting in higher Topt and Tsmax than Willamette soils. Interestingly, modeling of N mineralization using the two-pool model suggested that only the less-labile pool of SOM was contributing to N mineralization at most temperatures in all soils. The difference in labile and less-labile SOM pool utilization between respiration and N mineralization may suggest that these processes may not be as interconnected as previously thought.

In Vivo Estimation of Ketogenesis Using Metabolic Flux Analysis—Technical Aspects and Model Interpretation

Ketogenesis occurs in liver mitochondria where acetyl-CoA molecules, derived from lipid oxidation, are condensed into acetoacetate (AcAc) and reduced to β-hydroxybutyrate (BHB). During carbohydrate scarcity, these two ketones are released into circulation at high rates and used as oxidative fuels in peripheral tissues. Despite their physiological relevance and emerging roles in a variety of diseases, endogenous ketone production is rarely measured in vivo using tracer approaches. Accurate determination of this flux requires a two-pool model, simultaneous BHB and AcAc tracers, and special consideration for the stability of the AcAc tracer and analyte. We describe the implementation of a two-pool model using a metabolic flux analysis (MFA) approach that simultaneously regresses liquid chromatography-tandem mass spectrometry (LC-MS/MS) ketone isotopologues and tracer infusion rates. Additionally, 1H NMR real-time reaction monitoring was used to evaluate AcAc tracer and analyte stability during infusion and sample analysis, which were critical for accurate flux calculations. The approach quantifies AcAc and BHB pool sizes and their rates of appearance, disposal, and exchange. Regression analysis provides confidence intervals and detects potential errors in experimental data. Complications for the physiological interpretation of individual ketone fluxes are discussed.

Considering Soil Potassium Pools with Dissimilar Plant Availability

Soil potassium (K) has traditionally been portrayed as residing in four functional pools: solution K, exchangeable K, interlayer (sometimes referred to as “fixed” or “nonexchangeable”) K, and structural K in primary minerals. However, this four-pool model and associated terminology have created confusion in understanding the dynamics of K supply to plants and the fate of K returned to the soil in fertilizers, residues, or waste products. This chapter presents an alternative framework to depict soil K pools. The framework distinguishes between micas and feldspars as K-bearing primary minerals, based on the presence of K in interlayer positions or three-dimensional framework structures, respectively; identifies a pool of K in neoformed secondary minerals that can include fertilizer reaction products; and replaces the “exchangeable” K pool with a pool defined as “surface-adsorbed” K, identifying where the K is located and the mechanism by which it is held rather than identification based on particular soil testing procedures. In this chapter, we discuss these K pools and their behavior in relation to plant K acquisition and soil K dynamics.