"What would the world be if there were no hunger?" It's a question that Professor Crystal would ask her students. They found it hard to answer, she wrote later, because imagining something that isn't part of real life—and learning how to make it real—is a rare skill. It is taught to artists and engineers, but much less often to scientists. Crystal set out to change that, and helped to create a global movement. The result一an approach known as systems thinking—is now seen as essential in meeting global challenges.

Systems thinking is crucial to achieving targets such as zero hunger and better nutrition because it requires considering the way in which food is produced, processed, delivered and consumed, and looking at how those things intersect (交叉) with human health, the environment, economics and society. According to systems thinking, changing the food system—or any other network- requires three things to happen. First, researchers need to identify all the players in that system, second, they must work out how they relate to each other, and third, they need to understand and quantify the impact of those relationships on each other and on those outside the system.

Take nutrition. In the latest UN report on global food security, the number of undernourished (营养不良) people in the world has been rising, despite great advances in nutrition science. Tracking of 150 biochemicals in food has been important in revealing the relationships between calories, sugar, fat and the occurrence of common diseases. But using machine learning and artificial intelligence, some scientists propose that human diets consist of at least 26,000 biochemicals—and that the vast majority are not known. This shows that we have some way to travel before achieving the first objective of systems t hinking - which,in this example, is to identify more constituent parts of the nutrition system.

A systems approach to creating change is also built on the assumption that everyone in the system has equal power. But as some researchers find, the food system is not an equal one. A good way to redress (修正) such power imbalance is for more universities to do what Crystal did and teach students how to think using a systems approach.

More researchers, policymakers and representatives from the food industry must learn to look beyond their direct lines of responsibility and adopt a systems approach. Crystal knew that visions alone don't produce results, but concluded that "we'll never produce results that we can't envision".

If you ever get the impression that your dog can "tell" whether you look content or annoyed, you may be onto something. Dogs may indeed be able to distinguish between happy and angry human faces, according to a new study.

Researchers trained a group of 11 dogs to distinguish between images（图像）of the same person making either a happy or an angry face. During the training stage, each dog was shown only the upper half or the lower half of the person's face. The researchers then tested the dogs' ability to distinguish between human facial expressions by showing them the other half of the person's face or images totally different from the ones used in training. The researchers found that the dogs were able to pick the angry or happy face by touching a picture of it with their noses more often than one would expect by random chance.

The study showed the animals had figured out how to apply what they learned about human faces during training to new faces in the testing stage. "We can rule out that the dogs simply distinguish between the pictures based on a simple cue, such as the sight of teeth," said study author Corsin Muller. "Instead, our results suggest that the successful dogs realized that a smiling mouth means the same thing as smiling eyes, and the same rule applies to an angry mouth having the same meaning as angry eyes.”

"With our study, we think we can now confidently conclude that at least some dogs can distinguish human facial expressions," Muller told Live Science.

At this point, it is not clear why dogs seem to be equipped with the ability to recognize different facial expressions in humans. "To us, the most likely explanation appears to be that the basis lies in their living with humans, which gives them a lot of exposure to human facial expressions," and this exposure has provided them with many chances to learn to distinguish between them, Muller said.

Yes, having a big name in science will help get your paper published, a new study confirms. Involving hundreds of researchers reviewing an economics paper, the study found that reviewers were more likely to recommend acceptance when the paper was associated with a famous author compared to a lesser-known one.

The Matthew effect, a term coined in 1968 to describe this prejudice, has been a topic of concern among scientists for years. However, previous efforts to document this prejudice had limitations such as small sample sizes or lack of randomization. To address these issues, a team from the University of Innsbruck conducted an extensive study.

The team sent emails to about 3,300 researchers, inviting them to review an economics paper for a real journal. The paper had two authors: Vernon Smith, a Nobel Prize winner, and Sabiou Inoua, one of Smith's former Ph. D. students. The potential reviewers received one of three descriptions of the paper: one mentioning only Smith, another mentioning only Inoua, and a third with no author mentioned. Of the researchers who agreed to review the paper, Smith's fame influenced their responses. When given only Smith's name, 38.5% accepted the invitation to review, while the figures were 30.7% for those given no name and 28.5% for those given only Inoua's name.

To further avoid prejudice, the team randomly assigned the 313 reviewers who initially received no author's name to review one of three papers: one credited to Smith alone, another to Inoua alone,and a third with no authors listed. Reviewers rated the paper credited to Smith the highest, praising its inclusion of new information and data-supported conclusions. The version with no authors received recommendations for acceptance from 24% of reviewers, more than double the percentage for the version credited only to Inoua.

The team warned against evaluating identical work differently based on the author's identity and suggested that double-blind reviews may reduce the prejudice. However, this approach may not be effective as reviewers can often identify authors through preprints or conference presentations.