The yield and production of alfalfa (Medicago sativa L.) have not been significantly improved in Kansas for the last 30 years even though farmers are using improved varieties. We have noted a significant yield difference between average alfalfa yield reported by farmers and researchers. The magnitude of yield gap in Kansas and its underlying factors are still unknown. Thus, understanding of potential yield is essential to meet the future forage demand with the limited production resources. The main objective of this study was, therefore, to quantify the current yield gap and identify the main yield-limiting factor for rainfed alfalfa grown in Kansas. To achieve this objective, we selected 24 counties in Kansas based on the rainfed production area and total production, and used county-level yield, daily temperature, and rainfall data from the past 30 yrs (1988–2017) of those selected counties. We applied four statistical approaches: (i) probability distribution function to delineate county-level alfalfa growing season, (ii) stochastic frontier yield function to estimate optimum growing season rainfall (GSR) and attainable yield, (iii) linear boundary function to estimate minimum water loss, water use efficiency, and water-limited potential yield, and (iv) conditional inference tree to identify the major yield contributing weather variables. The probability distribution function delineated the alfalfa growing season starting from mid-March to mid-November in Kansas. The frontier model estimated the attainable yield of 9.2 Mg ha⁻¹ at an optimum GSR of 664 mm, generating a current yield gap of 18%. The linear boundary function estimated the water-limited potential yield of 15.5 Mg ha⁻¹ at an existing GSR of 624 mm, generating a yield gap of 50%. The conditional inference tree revealed that 24% of the variation in rainfed alfalfa yield in Kansas was explained by weather variables, mainly due to GSR followed minimum temperature. However, we found only 7% GSR deficit in the study area, indicating that GSR is not the only cause for such a wide yield gap. Thus, further investigation of other yield-limiting management factors is essential to minimize the current yield gap. The statistical models used in this study might be particularly useful when yield estimation using remote sensing and crop simulation models are not applicable in terms of time, resources, facilities, and investments.

The high antibody doses required to achieve a therapeutic effect often necessitate high-concentration products that can lead to challenging viscosity issues in production and delivery. Predicting antibody viscosity in early development can play a pivotal role in reducing late-stage development costs. In recent years, numerous efforts have been made to predict antibody viscosity through dilute solution measurements. A key finding is that the entanglement of long, flexible complexes contributes to the sharp rise in antibody viscosity at the required dosing. This entanglement model establishes a connection between the two-body binding affinity and the many-body viscosity. Exploiting this insight, this study connects dilute solution measurements of self-association to high-concentration viscosity profiles to quantify the relationship between these regimes. The resulting model has exhibited success in predicting viscosity at high concentrations (around 150 mg/mL) from dilute solution measurements, with only a few outliers remaining. Our physics-based approach provides an understanding of fundamental physics, interpretable connections to experimental data, the potential to extrapolate beyond training conditions, and the capacity to effectively explain the physical mechanics behind these outliers. Conducting hypothesis-driven experiments that specifically target the viscosity and relaxation mechanisms of outlier molecules may allow us to unravel the intricacies of their behavior and, in turn, enhance the performance of our model.

Aberrant aggregation of proteins into amyloid fibrils is associated with numerous neurodegenerative, systemic and metabolic diseases. Amyloidogenic proteins undergo spontaneous liquid-liquid phase separation (LLPS), rapidly forming protein-rich condensates prior to fibrillization. However, the exact effects of LLPS on amyloid aggregation remain unclear as contrasting fibrillization-promotion, inhibition and even biphasic effects have been reported in the literature. In this study, we integrate LLPS-induced heterogeneity of protein concentrations into a thermodynamic-kinetic model of amyloid aggregation. We adopt the phase transition theory and introduce protein condensates as an additional protein state alongside non-interacting monomers, oligomers and fibrils. Oligomerization and fibrillization can occur both in the protein-rich condensates and the protein-poor solution. This model allows us to derive the time evolution of different states – monomers, condensates, oligomers, and fibrils – spanning a wide range of concentrations, and determine how model parameters related to LLPS, fibrillization, and oligomerization influence fibrillization kinetics. Using this global model, we resolve the seemingly contradictory effects of LLPS on fibrillization. We expect the developed thermodynamic-kinetic model of LLPS, and amyloid aggregation will help advance our understanding, modulation, and mitigation of pathological aggregation processes in amyloid diseases. TOC:

Biomolecular condensates appear throughout the cell serving a wide variety of functions. Many condensates appear to form by the assembly of multivalent molecules, which produce phase separated networks with liquid-like properties. These networks then recruit client molecules, with the total composition providing functionality. Here we use a model system of poly-SUMO and poly-SIM proteins to understand client-network interactions and find that the structure of the network plays a strong role in defining client recruitment, and thus functionality. The basic unit of assembly in this system is a zipper-like filament composed of alternating poly-SUMO and poly-SIM molecules. These filaments have defects of unsatisfied bonds that allow for both the formation of a 3D network and the recruitment of clients. The filamentous structure constrains the scaffold stoichiometries and the distribution of client recruitment sites that the network can accommodate. This results in a non-monotonic client binding response that can be tuned independently by the client valence and binding energy. These results show how the interactions within liquid states can be disordered yet still contain structural features that provide functionality to the condensate.