Biological macromolecules, such as proteins and polysaccharides, are widely used in food systems because their interactions impart a desirable texture to food products. Plant proteins interact with food components via...Biological macromolecules, such as proteins and polysaccharides, are widely used in food systems because their interactions impart a desirable texture to food products. Plant proteins interact with food components via protein-protein and protein-polysaccharide associations, and the formation of a matrix, which can entrap other food components such as water, lipids and flavors. These networks provide structural integrity to food products and can serve as important functional ingredients in processed foods. Intermolecular interactions of typical polysaccharides result either in simple associations or in the form of a double or triple helix. The linear double helical segments may then interact to form a super junction and a three-dimensional gel network. The formation of these structural networks takes place during processing and involves the transformation from a liquid or viscous sol into a solid material with elastic properties. Interests in the behavior of mixed gels center on the prospects of enhanced flexibility in their mechanical and structural properties compared to those of pure gels. Findings on molecular interactions between plant proteins （e.g., soy, canola and pea proteins） and polysaccharides （e.g., guar gum, carrageenan, and pectin） allow for the modification of physical and textural characteristics of mixed biopolymers to meet desired functional property.展开更多
Aims The limitations of classical Lotka–Volterra models for analyzing and interpreting competitive interactions among plant species have become increasingly clear in recent years.Three of the problems that have been ...Aims The limitations of classical Lotka–Volterra models for analyzing and interpreting competitive interactions among plant species have become increasingly clear in recent years.Three of the problems that have been identified are(i)the absence of frequency-dependence,which is important for long-term coexistence of species,(ii)the need to take unmeasured(often unmeasurable)variables influencing individual performance into account(e.g.spatial variation in soil nutrients or pathogens)and(iii)the need to separate measurement error from biological variation.Methods We modified the classical Lotka–Volterra competition models to address these limitations.We fitted eight alternative models to pin-point cover data on Festuca ovina and Agrostis capillaris over 3 years in an herbaceous plant community in Denmark.A Bayesian modeling framework was used to ascertain whether the model amendments improve the performance of the models and increase their ability to predict community dynamics and to test hypotheses.Important Findings Inclusion of frequency-dependence and measurement error,but not unmeasured variables,improved model performance greatly.Our results emphasize the importance of comparing alternative models in quantitative studies of plant community dynamics.Only by considering possible alternative models can we identify the forces driving community assembly and change,and improve our ability to predict the behavior of plant communities.展开更多
文摘Biological macromolecules, such as proteins and polysaccharides, are widely used in food systems because their interactions impart a desirable texture to food products. Plant proteins interact with food components via protein-protein and protein-polysaccharide associations, and the formation of a matrix, which can entrap other food components such as water, lipids and flavors. These networks provide structural integrity to food products and can serve as important functional ingredients in processed foods. Intermolecular interactions of typical polysaccharides result either in simple associations or in the form of a double or triple helix. The linear double helical segments may then interact to form a super junction and a three-dimensional gel network. The formation of these structural networks takes place during processing and involves the transformation from a liquid or viscous sol into a solid material with elastic properties. Interests in the behavior of mixed gels center on the prospects of enhanced flexibility in their mechanical and structural properties compared to those of pure gels. Findings on molecular interactions between plant proteins （e.g., soy, canola and pea proteins） and polysaccharides （e.g., guar gum, carrageenan, and pectin） allow for the modification of physical and textural characteristics of mixed biopolymers to meet desired functional property.
文摘Aims The limitations of classical Lotka–Volterra models for analyzing and interpreting competitive interactions among plant species have become increasingly clear in recent years.Three of the problems that have been identified are(i)the absence of frequency-dependence,which is important for long-term coexistence of species,(ii)the need to take unmeasured(often unmeasurable)variables influencing individual performance into account(e.g.spatial variation in soil nutrients or pathogens)and(iii)the need to separate measurement error from biological variation.Methods We modified the classical Lotka–Volterra competition models to address these limitations.We fitted eight alternative models to pin-point cover data on Festuca ovina and Agrostis capillaris over 3 years in an herbaceous plant community in Denmark.A Bayesian modeling framework was used to ascertain whether the model amendments improve the performance of the models and increase their ability to predict community dynamics and to test hypotheses.Important Findings Inclusion of frequency-dependence and measurement error,but not unmeasured variables,improved model performance greatly.Our results emphasize the importance of comparing alternative models in quantitative studies of plant community dynamics.Only by considering possible alternative models can we identify the forces driving community assembly and change,and improve our ability to predict the behavior of plant communities.
