Crop scientists break down barriers at ames meeting

For years, crop science has been balkanized, with specialists in rice, corn, and soybeans, for example, working on their own commodities and attending their own meetings. But at the First International Crop Science Congress, held in July in Ames, Iowa-an 8-day event 3 years in the making-the discipline displayed a newfound hybrid vigor. More than 1000 researchers of various persuasions, including plant molecular biology, classical plant breeding, agronomy, and soil science, representing 85 countries, shared their expertise in basic and applied studies. Here are a couple of proposals for expanding world food production and another that shows the diverse roles crops can play.     

Excitation and decay of correlated atomic States

Doubly excited states of atoms and ions in which two electrons are excited from the ground configuration display strong radial and angular electron correlations. They are prototypical examples of quantum-mechanical systems with strong coupling. Two distinguishing characteristics of these states are: (i) their organization into successive families, with only weak coupling between families, and (ii) a hierarchical nature of this coupling, with states from one family decaying primarily to those in the next lower family. A view of the pair of electrons as a single entity, with the electron-electron repulsion between them divided into an adiabatic and a nonadiabatic piece, accounts for many of the dominant features. The stronger, adiabatic part determines the family structure and the weaker, nonadiabatic part the excitation and decay between successive families. Similar considerations extend to three-electron atomic states, which group into five different classes. They are suggestive of composite models for quarks in elementary particle physics, which exhibit analogous groupings into families with a hierarchical arrangement of masses and electroweak decays.     

NMR diffraction and spatial statistics of stationary systems

Nuclear magnetic resonance (NMR) spatial imaging data may be acquired, processed, and interpreted in ways that provide information directly analogous to diffraction experiments, with length scales determined by gradient strengths rather than radiation wavelengths. This approach, originally considered by Mansfield nearly two decades ago, provides access to autocorrelations of sample density that statistically characterize small-scale density variations. These NMR "Patterson functions" can be acquired orders of magnitude more rapidly than comparably resolved NMR images and are suitable for spatial characterization of small features in bulk samples, such as morphology in structural materials. Unlike hindered diffusion approaches, neither mobility, penetrants, nor transport time are required for examining granularity and porosity.