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The world is facing a potential food crisis, with soaring prices and millions in danger of severe hunger, as the war in Ukraine threatens supplies of key staple crops, the UN Food and Agriculture Organization has warned. Máximo Torero, its chief economist, said food prices were already high before Russia's invasion, owing to the effects of the pandemic. He said the strain of war could tip the global food system into disaster: “We were already having problems with food prices. What countries are doing now is exacerbating that. The war is putting us in a situation where we could easily fall into a food crisis.”
At least 50 countries depend on Russia and Ukraine for 30% or more of their wheat supply, and many developing countries in northern Africa, Asia and the near east are among the most reliant. Poor countries are bearing the brunt of the price increases. “My greatest fear is the conflict continues-then we will have a situation of significant levels of food price rises, in poor countries that were already in an extremely weak financial situation owing to COVID-19,” Torero said. “The number of chronically hungry people will grow significantly, if that is the case.”
Ukraine alone supplied 12% of global wheat before the war, and was the biggest producer of sunflower oil. About two-thirds of its wheat exports had already been delivered before the invasion, but the rest is now blocked, and farmers may be unable to continue with spring planting, or take in grain harvests in the summer. But the crisis goes deeper: Ukraine and Russia are also major producers of fertiliser, prices for which had already leapt under high energy prices-and the war is sending energy prices higher still.
The worst food price rises in recent memory struck in 2007-08 and in 2010-12, caused by high energy prices followed by poor weather. Those sudden peaks contributed to riots and political upheavals, the shocks of which are still being felt.
The agriculture ministers of the G7 group of richest countries met on Friday to coordinate a response, urging countries to keep markets open. Torero urged countries to keep food systems open and to share information on stocks, harvests and food availability, to try to even out supply issues; countries that were in a position to produce more should do so. “Right now, the short-term problem is availability. We need to find ways to fill the gap (in production caused by the war). We think the gap can be closed somewhat, but not 100%. Countries should also try to diversify their suppliers,” he said. Even if the conflict were to be resolved quickly, the impacts would be felt for some time, Torero said.
[A] Widespread famine.
[B] Worsening pandemic.
[C] Poor harvest of the main crops.
[D] Military conflict in grain producing areas.
[A] Grain importing countries.
[B] People lack of food supply.
[C] Regions affected by COVID-19.
[D] Economically backward countries.
[A] the root cause of food price rises
[B] the trend of food price changes
[C] the possible aftermath of food crisis
[D] the link between economy and politics
[A] might cause riots
[B] may be long-lasting
[C] could get worsened
[D] will be solved soon
[A] War Is Exacerbating Food Crisis
[B] Food Prices and Social Unrest
[C] Joint Action Is Needed for Food Safety
[D] Causes and Effects of Food Shortage
An international team of scientists has identified the neural mechanisms through which sound relieves pain in mice, whose findings could inform development of safer methods to treat pain. “We need more effective methods of managing acute and chronic pain, and that starts with gaining a better understanding of the basic neural processes that regulate pain,” said a researcher. “By uncovering the brain circuitry that mediates the pain-reducing effects of sound in mice, this study adds critical knowledge that could ultimately inform new approaches for pain therapy.”
Dating back to 1960, studies in humans have shown that music and other kinds of sound can help relieve acute and chronic pain, including pain from dental and medical surgery, labor and delivery, and cancer. However, how the brain produces this pain reduction was less clear. “Human brain imaging studies have implicated certain areas of the brain in music-induced pain relief, but these are only associations,” said a co-senior author. “In animals, we can more fully explore and manipulate the circuitry to identify the neural layers involved.”
The researchers first exposed wounded mice to three types of sound: a pleasant piece of classical music, an unpleasant rearrangement of the same piece, and white noise. Surprisingly, all three types of sound, when played at a low intensity relative to background noise (about the level of a whisper) reduced pain sensitivity in the mice. Higher intensities of the same sounds had no effect on animals' pain responses. The researchers were really surprised that the intensity of sound, and not the category or perceived pleasantness of sound would matter.
To explore the brain circuitry underlying this effect, the researchers used non-infectious viruses coupled with fluorescent proteins to trace connections between brain regions. They identified a route from auditory cortex, which receives and processes information about sound, to the thalamus, which acts as a relay station for sensory signals, including pain, from the body. In freely moving mice, low-intensity white noise reduced the activity of neurons at the receiving end of the pathway in the thalamus. In the absence of sound, suppressing the pathway mimicked the pain-relieving effects of low-intensity noise, while turning on the pathway restored animals' sensitivity to pain.
It is unclear if similar brain processes are involved in humans, or whether other aspects of sound, such as its perceived harmony or pleasantness, are important for human pain relief. “We don't know if human music means anything to rodents, but it has many different meanings to humans-you have a lot of emotional components,” a co-author said. The results could give scientists a starting point for studies to determine whether the animal findings apply to humans, and ultimately could inform development of safer alternatives for treating pain.
[A] how to manage different pain
[B] how sound reduces pain in mice
[C] how to suppress pain more safely
[D] how the brain circuitry mediates sound
[A] associate music with pain treatment
[B] verify the findings of the 1960 studies
[C] learn the neural processes of pain relief
[D] identify the brain areas that produce pain
[A] A sweet song.
[B] Traffic noises.
[C] A low-intensity sound.
[D] Thundering rock music.
[A] the thalamus
[B] the auditory cortex
[C] fluorescent proteins
[D] non-infectious viruses
[A] human beings rely on emotional components to relieve pain
[B] the researchers will develop safer alternatives for pain relief
[C] human brain processes are much more complex than animals
[D] the mentioned findings may not apply to humans
Charter schools looking for federal start-up grants would face stricter requirements under new rules proposed by the Biden administration. Charter schools are publicly funded but privately run enterprises. They were meant to serve as laboratories of innovation as well as provide alternatives for families unhappy with their local public schools. They were once seen as a popular middle ground in the school-choice debate.
The biggest proposed change would affect the for-profit management companies that often run charter schools. To qualify for grants, charter schools must, by law, be run by nonprofit groups. Many, however, outsource the operation to for-profit companies, and those arrangements have been eligible for the federal startup money. That would change. Nonprofits could outsource a particular task-such as payroll, for instance-to for-profit companies. But arrangements in which for-profit companies run the entire operation under contracts known as “sweeps” would be ineligible for the start-up grants. The proposal specifically bars arrangements under which a for-profit management company “exercises full or substantial administrative control over the charter school or over programmatic decisions.”
Michael Petrilli, president of the Thomas B. Fordham Institute and a supporter of charter schools, said some new limits on for-profit arrangements are welcome. But the rules could wind up so sweeping as to pull in more fair arrangements that work well. “It looks like an aggressive attempt to keep schools managed by for-profit companies from receiving these funds,” he said.
Carol Burris, a longtime charter school critic who is executive director of the Network for Public Education, welcomed the change as overdue but predicted limited impact. “The for-profit operators who are creating these schools don't need this money to open up schools. They'll continue to open schools. They just won't get federal grants to do it.”
The proposed requirements would also toughen rules for nonprofit operators to qualify for the program. Applicants must submit a community impact analysis demonstrating that there is “sufficient demand” for the new school and that the project would meet the needs of students and families in the community. They would also have to detail how the applicant would create racially and socioeconomically diverse student and staff populations, though if this is not possible given the community demographics, applications could still be funded. To show “unmet demand,” applicants are asked to cite data about any over-enrolled existing public schools.
That will be difficult for many applicants, Petrilli predicted, because enrollments are dropping all over the country. Burris noted that the proposal does not say it will refuse to fund charters in areas with falling enrollment, just that it will be considered. “And doesn't that make sense from the standpoint of the taxpayer?” she asked. “Wouldn't you rather see a charter school in an area where it's needed? For the first time, the department is requiring the applicant to talk about the impact the new charter school will have on the community.”
[A] They are welcomed by some families.
[B] They put pressure on public schools.
[C] They rely on federal funds to maintain daily operations.
[D] They are confronted with tighter rules than public schools.
[A] participating in school operation
[B] applying for government subsidies
[C] taking tasks by “sweeps” contracts
[D] exerting financial influence on schools
[A] the necessity of the federal start-up grants
[B] the equity of school operation mechanism
[C] the complexity of the funding application
[D] the actual effect of the new rules
[A] ensure the ethnic diversity of the community
[B] demonstrate “sufficient demand” for new schools
[C] avoid the possibility of over-enrollment
[D] provide the community demographics
[A] aggressive
[B] necessary
[C] impractical
[D] formalistic
Do you like the thick brush strokes and soft color palettes of an impressionist painting such as those by Claude Monet? Or do you prefer the bold colors and abstract shapes of a Rothko? Individual art tastes have a certain mystique, but now a new Caltech study shows that a simple computer program can accurately predict which paintings a person will like.
The new study enlisted more than 1,500 volunteers to rate paintings in the genres of impressionism, cubism, abstract, and color field. The volunteers' answers were fed into a computer program and then, after this training period, the computer could predict the volunteers' art preferences much better than would happen by chance.
In the study, the team programmed the computer to break a painting's visual attributes down into what they called low-level features-traits like contrast, saturation, and hue-as well as high-level features, which require human judgment and include traits such as whether the painting is dynamic or still. The computer program then estimates how much a specific feature is taken into account when making a decision about how much to like a particular piece of art. Both the low-and high-level features are combined together when making these decisions. Once the computer has estimated that, then it can successfully predict a person's liking for another previously unseen piece of art.
What is more, the researchers found that they could also train a deep convolutional neural network (DCNN) to learn to predict the volunteer's art preferences with a similar level of accuracy. A DCNN is a type of machine-learning program, in which a computer is fed a series of training images so that it can learn to classify objects, such as cats versus dogs. These neural networks have units that are connected to each other like neurons in a brain. By changing the strength of the connection of one unit to another, the network can “learn”. In this case, the deep-learning approach did not include any of the selected low-or high-level visual features used in the first part of the study, so the computer had to “decide” what features to analyze on its own.
While the computer program was successful at predicting the volunteers' art preferences, the researchers say there is still more to learn about the subtle differences that go into any one individual's taste. “There are aspects of preferences unique for a given individual that we have not succeeded in explaining using this method,” says the researchers. “It still may be possible to identify and learn about those features in a computer model, but to do so will involve a more detailed study of each individual's preferences in a way that may not generalize across individuals as we found here.”
[A] ratings of different paintings
[B] paintings' low-level features
[C] paintings' high-level features
[D] features of noted painting styles
[A] need human judgment during analysis
[B] can not be decomposed into multiple traits
[C] are involved in predicting one's art liking
[D] include paintings' dynamic characteristics
[A] It is a brain neural reinforcing network.
[B] It is a computer “learning” program.
[C] It is a training system for the testee.
[D] It is an advanced photo shooter.
[A] different artistic genres
[B] individual's unique art preferences
[C] generalization of art tastes
[D] subtle features of paintings
[A] Individual preference for art still remains unpredictable.
[B] DCNN is a kind of machine-learning network program.
[C] Artificial intelligence has new development in art field.
[D] Computers can be used to predict people's tastes in art.